
import platform
import socket
import psutil

print("\n\nAVAILABLE SYSTEM RESOURCES FOR CAPSTONE PROJECT:\n")
print("***************************************************************************\n\n")
print("PLATFORM - ",platform.system())
print("")
print("PLATFORM - RELEASE - ",platform.release())
print("")
print("PLATFORM - VERSION - ",platform.version())
print("")
print("ARCHITECTURE - ",platform.machine())
print("")
print("HOST NAME - ",socket.gethostname())
print("")
print("PROCESSOR - ",platform.processor())
print("")
print("RAM - ",str(round(psutil.virtual_memory().total / (1024.0 **3)))+" GB")
print("\n\n***************************************************************************\n\n")


AVAILABLE SYSTEM RESOURCES FOR CAPSTONE PROJECT:

***************************************************************************


PLATFORM -  Windows

PLATFORM - RELEASE -  10

PLATFORM - VERSION -  10.0.22621

ARCHITECTURE -  AMD64

HOST NAME -  MSI

PROCESSOR -  Intel64 Family 6 Model 141 Stepping 1, GenuineIntel

RAM -  8 GB


***************************************************************************


# Import all the necessary libraries

import numpy as np
import pandas as pd


%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.gridspec as gridspec
import seaborn as sns
from PIL import Image
from IPython.display import display


import os
import shutil
from pathlib import Path
import pathlib
from zipfile import ZipFile
from glob import glob
import six.moves.urllib as urllib
import sys
import tarfile

from collections import defaultdict
from io import StringIO

import cv2
import pydicom as dicom
from pydicom.pixel_data_handlers import convert_color_space
from pydicom.pixel_data_handlers.util import apply_color_lut

from fastai.basics import *
from fastai.callback.all import *
from fastai.vision.all import *
from fastai.medical.imaging import *

from tqdm import tqdm

import pickle as pkl

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.metrics import roc_auc_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import plot_confusion_matrix
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

import tensorflow as tf
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential  # initial NN
from tensorflow.keras.layers import Activation, Dense, Dropout, BatchNormalization # construct each layer
from tensorflow.keras.layers import Conv2D, SeparableConv2D 
from tensorflow.keras.layers import MaxPooling2D # swipe across by pool size
from tensorflow.keras.layers import Flatten, GlobalAveragePooling2D, GlobalMaxPooling2D
from tensorflow.keras.optimizers import Adam, SGD, RMSprop
from tensorflow.keras.preprocessing import image
from tensorflow.keras.metrics import Precision, Recall, SparseCategoricalAccuracy
from tensorflow.keras.applications.vgg16 import preprocess_input,decode_predictions
from tensorflow.keras.applications.inception_v3 import preprocess_input
from tensorflow.keras.applications import MobileNet
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2
from tensorflow.keras.applications.densenet import DenseNet169
from tensorflow.keras.applications.densenet import DenseNet121
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, Reshape

from keras.callbacks import EarlyStopping
from keras.utils.vis_utils import plot_model
from keras.models import model_from_json
from keras.preprocessing.image import ImageDataGenerator
from keras.applications.imagenet_utils import preprocess_input
from keras.callbacks import ModelCheckpoint
from keras.callbacks import ReduceLROnPlateau
from keras.applications.vgg16 import VGG16
from keras.layers import Input
from keras.callbacks import EarlyStopping,ReduceLROnPlateau

from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

import warnings 
warnings.filterwarnings('ignore')


train_label = pd.read_csv("stage_2_train_labels.csv")


train_class = pd.read_csv("stage_2_detailed_class_info.csv")


train_label.head(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


train_label.duplicated().sum()

0


train_class.head(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


train_final = pd.merge(train_label, train_class, on ='patientId')


train_final.head(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


test_submission = pd.read_csv("stage_2_sample_submission.csv")


test_submission.head(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


test_submission.duplicated().sum()

0


train_final.shape

(37629, 7)


train_final.describe()


train_duplicates_count = train_final.duplicated().sum()


train_duplicates_count

7402


train_duplicates = train_final[train_final.duplicated()]


train_duplicates.head(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


train_final_non_dups = train_final.drop_duplicates(keep='first')


train_final_non_dups = train_final_non_dups.reset_index(drop=True)


train_final.shape

(37629, 7)


train_final_non_dups.shape

(30227, 7)


37629-30227

7402


train_meta_df = train_final_non_dups.copy()


train_meta_df.head(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


train_meta_df.dtypes

patientId     object
x            float64
y            float64
width        float64
height       float64
Target         int64
class         object
dtype: object


train_Lung_Opacity = train_meta_df[train_meta_df["class"] == "Lung Opacity"]
train_Normal = train_meta_df[train_meta_df["class"] == "Normal"]
train_Not_Normal = train_meta_df[train_meta_df["class"] == "No Lung Opacity / Not Normal"]


print("len of Lung opacity",len(train_Lung_Opacity))
print("len of Normal",len(train_Normal))
print("len of Not Normal",len(train_Not_Normal))
print("")

len of Lung opacity 9555
len of Normal 8851
len of Not Normal 11821


Lung_Opacity_Unique = train_Lung_Opacity["patientId"].unique()
Normal_Unique = train_Normal["patientId"].unique()
Not_Normal_Unique = train_Not_Normal["patientId"].unique()


print("No. of Unique Pneumonia PatientIds",len(Lung_Opacity_Unique))
print("No. of Unique Normal PatientIds",len(Normal_Unique))
print("No. of Unique Not Normal",len(Not_Normal_Unique))

No. of Unique Pneumonia PatientIds 6012
No. of Unique Normal PatientIds 8851
No. of Unique Not Normal 11821


print(6012+8851+11821)

26684


print(Lung_Opacity_Unique[:5])
print("")
print(Normal_Unique[:5])
print("")
print(Not_Normal_Unique[:5])
print("")

['00436515-870c-4b36-a041-de91049b9ab4'
 '00704310-78a8-4b38-8475-49f4573b2dbb'
 '00aecb01-a116-45a2-956c-08d2fa55433f'
 '00c0b293-48e7-4e16-ac76-9269ba535a62'
 '00f08de1-517e-4652-a04f-d1dc9ee48593']

['003d8fa0-6bf1-40ed-b54c-ac657f8495c5'
 '009482dc-3db5-48d4-8580-5c89c4f01334'
 '009eb222-eabc-4150-8121-d5a6d06b8ebf'
 '00a85be6-6eb0-421d-8acf-ff2dc0007e8a'
 '00f87de5-5fe0-4921-93ea-914d7e683266']

['0004cfab-14fd-4e49-80ba-63a80b6bddd6'
 '00313ee0-9eaa-42f4-b0ab-c148ed3241cd'
 '00322d4d-1c29-4943-afc9-b6754be640eb'
 '00569f44-917d-4c86-a842-81832af98c30'
 '006cec2e-6ce2-4549-bffa-eadfcd1e9970']


with ZipFile('stage_2_train_images.zip', 'r') as z:
  z.extractall()


with ZipFile('stage_2_test_images.zip', 'r') as z:
  z.extractall()


dst1 = "Training"
dst2 = "Lung Opacity"
dst3 = "Normal"
dst4 = "Not Normal"

os.mkdir(dst1)
path1 = os.path.join(dst1,dst2)
path2 = os.path.join(dst1,dst3)
path3 = os.path.join(dst1,dst4)

mode = 0o777

os.mkdir(path1, mode)
os.mkdir(path2, mode)
os.mkdir(path3, mode)


# Source path
source = "stage_2_train_images"
 
# Destination path
destination = "Training/Lung Opacity"

for item in Lung_Opacity_Unique:
    file = item+".dcm"
    src_file = os.path.join(source+"/"+file)
    dst_file = os.path.join(destination+"/"+file)
    print("src_file",src_file)
    print("dst_file",dst_file)
    shutil.copyfile(src_file, dst_file)


# Source path
source = "stage_2_train_images"
 
# Destination path
destination = "Training/Normal"

for item in Normal_Unique:
    file = item+".dcm"
    src_file = os.path.join(source+"/"+file)
    dst_file = os.path.join(destination+"/"+file)
    print("src_file",src_file)
    print("dst_file",dst_file)
    shutil.copyfile(src_file, dst_file)


# Source path
source = "stage_2_train_images"
 
# Destination path
destination = "Training/Not Normal"

for item in Not_Normal_Unique:
    file = item+".dcm"
    src_file = os.path.join(source+"/"+file)
    dst_file = os.path.join(destination+"/"+file)
    print("src_file",src_file)
    print("dst_file",dst_file)
    shutil.copyfile(src_file, dst_file)


Lung_opa_list = os.listdir("Training/Lung Opacity") # your directory path
Lung_opa_number_files = len(Lung_opa_list)
print('\nNumber of images in Lung Opacity class after seggregation:',Lung_opa_number_files)

Normal_list = os.listdir("Training/Normal") # your directory path
Normal_number_files = len(Normal_list)
print('\nNumber of images in Normal class after seggregation:',Normal_number_files)

Not_Normal_list = os.listdir("Training/Not Normal") # your directory path
Not_Normal_number_files = len(Not_Normal_list)
print('\nNumber of images in Not Normal class after seggregation:',Not_Normal_number_files)

Number of images in Lung Opacity class after seggregation: 6012

Number of images in Normal class after seggregation: 8851

Number of images in Not Normal class after seggregation: 11821


print("\nNo. of Unique Pneumonia PatientIds :",len(Lung_Opacity_Unique))
print("\nNo. of Unique Normal PatientIds    :",len(Normal_Unique))
print("\nNo. of Unique Not Normal           :",len(Not_Normal_Unique))

No. of Unique Pneumonia PatientIds : 6012

No. of Unique Normal PatientIds    : 8851

No. of Unique Not Normal           : 11821


image_dir_path = "Training"

paths = [path.parts[-3:] for path in
         Path(image_dir_path).rglob('*.dcm')]
Images_df = pd.DataFrame(data=paths, columns=['Folder', 'Image_Class', 'Image_file'])


Images_df.head(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


Images_df.tail(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


Images_df["Full_filename"] = Images_df["Folder"]+"/"+Images_df["Image_Class"]+"/"+Images_df["Image_file"]


Images_df.head(25).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


Images_df.describe()


# creating instance of labelencoder
labelencoder = LabelEncoder()
# Assigning numerical values and storing in another column
Images_df['Image_Class_Category'] = labelencoder.fit_transform(Images_df['Image_Class'])


Images_df['Image_Target'] = Images_df['Image_Class'].apply(lambda x: 1 if x == 'Lung Opacity' else 0)


Images_df.dtypes

Folder                  object
Image_Class             object
Image_file              object
Full_filename           object
Image_Class_Category     int32
Image_Target             int64
dtype: object


Images_df.to_pickle("Images_df.pkl")


unpickled_df = pd.read_pickle("Images_df.pkl")


Images_df = unpickled_df.copy()


Images_df.dtypes

Folder                  object
Image_Class             object
Image_file              object
Full_filename           object
Image_Class_Category     int32
Image_Target             int64
dtype: object


Images_df.head(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


def show_images(passed_value):
    show_df = Images_df.sample(n=passed_value)
    if passed_value/5 == 0:
        z = math.ceil(passed_value/5)
    else:
            z = math.ceil((passed_value)/5)

    fig, ax = plt.subplots(z,5,constrained_layout=True)
    fig.set_figheight(20)
    fig.set_figwidth(20)
    
    i = 0
    j = 0

    for index, row in show_df.iterrows():
        image_path = row['Full_filename']
        ds = dicom.dcmread(image_path)
        img = ds.pixel_array

        height_1 = 224
        width_1 = 224
        dim = (width_1, height_1)
        img_resize = cv2.resize(img, dim, interpolation=cv2.INTER_LINEAR)

        ax[i][j].imshow(img_resize, cmap=plt.cm.bone)
        ax[i][j].set_title(row['Image_Class'])
        
        j = j+1
        if j == 5:
            i = i+1
            j = 0
    plt.figure().clear()
    plt.show()
    return(show_df)


show_images(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})

<Figure size 640x480 with 0 Axes>


img = 'stage_2_train_images/0004cfab-14fd-4e49-80ba-63a80b6bddd6.dcm'

sample_md = dicom.filereader.dcmread(img)

sample_md

Dataset.file_meta -------------------------------
(0002, 0000) File Meta Information Group Length  UL: 202
(0002, 0001) File Meta Information Version       OB: b'\x00\x01'
(0002, 0002) Media Storage SOP Class UID         UI: Secondary Capture Image Storage
(0002, 0003) Media Storage SOP Instance UID      UI: 1.2.276.0.7230010.3.1.4.8323329.28530.1517874485.775526
(0002, 0010) Transfer Syntax UID                 UI: JPEG Baseline (Process 1)
(0002, 0012) Implementation Class UID            UI: 1.2.276.0.7230010.3.0.3.6.0
(0002, 0013) Implementation Version Name         SH: 'OFFIS_DCMTK_360'
-------------------------------------------------
(0008, 0005) Specific Character Set              CS: 'ISO_IR 100'
(0008, 0016) SOP Class UID                       UI: Secondary Capture Image Storage
(0008, 0018) SOP Instance UID                    UI: 1.2.276.0.7230010.3.1.4.8323329.28530.1517874485.775526
(0008, 0020) Study Date                          DA: '19010101'
(0008, 0030) Study Time                          TM: '000000.00'
(0008, 0050) Accession Number                    SH: ''
(0008, 0060) Modality                            CS: 'CR'
(0008, 0064) Conversion Type                     CS: 'WSD'
(0008, 0090) Referring Physician's Name          PN: ''
(0008, 103e) Series Description                  LO: 'view: PA'
(0010, 0010) Patient's Name                      PN: '0004cfab-14fd-4e49-80ba-63a80b6bddd6'
(0010, 0020) Patient ID                          LO: '0004cfab-14fd-4e49-80ba-63a80b6bddd6'
(0010, 0030) Patient's Birth Date                DA: ''
(0010, 0040) Patient's Sex                       CS: 'F'
(0010, 1010) Patient's Age                       AS: '51'
(0018, 0015) Body Part Examined                  CS: 'CHEST'
(0018, 5101) View Position                       CS: 'PA'
(0020, 000d) Study Instance UID                  UI: 1.2.276.0.7230010.3.1.2.8323329.28530.1517874485.775525
(0020, 000e) Series Instance UID                 UI: 1.2.276.0.7230010.3.1.3.8323329.28530.1517874485.775524
(0020, 0010) Study ID                            SH: ''
(0020, 0011) Series Number                       IS: '1'
(0020, 0013) Instance Number                     IS: '1'
(0020, 0020) Patient Orientation                 CS: ''
(0028, 0002) Samples per Pixel                   US: 1
(0028, 0004) Photometric Interpretation          CS: 'MONOCHROME2'
(0028, 0010) Rows                                US: 1024
(0028, 0011) Columns                             US: 1024
(0028, 0030) Pixel Spacing                       DS: [0.14300000000000002, 0.14300000000000002]
(0028, 0100) Bits Allocated                      US: 8
(0028, 0101) Bits Stored                         US: 8
(0028, 0102) High Bit                            US: 7
(0028, 0103) Pixel Representation                US: 0
(0028, 2110) Lossy Image Compression             CS: '01'
(0028, 2114) Lossy Image Compression Method      CS: 'ISO_10918_1'
(7fe0, 0010) Pixel Data                          OB: Array of 142006 elements


items_train = get_dicom_files('stage_2_train_images/')

dicom_train = pd.DataFrame.from_dicoms(items_train).reset_index(drop = True)


dicom_train.to_csv("dicom_train.csv", index = False)


dicom_train = pd.read_csv("dicom_train.csv")

dicom_train.columns

Index(['SpecificCharacterSet', 'SOPClassUID', 'SOPInstanceUID', 'StudyDate',
       'StudyTime', 'AccessionNumber', 'Modality', 'ConversionType',
       'ReferringPhysicianName', 'SeriesDescription', 'PatientName',
       'PatientID', 'PatientBirthDate', 'PatientSex', 'PatientAge',
       'BodyPartExamined', 'ViewPosition', 'StudyInstanceUID',
       'SeriesInstanceUID', 'StudyID', 'SeriesNumber', 'InstanceNumber',
       'PatientOrientation', 'SamplesPerPixel', 'PhotometricInterpretation',
       'Rows', 'Columns', 'MultiPixelSpacing', 'PixelSpacing', 'PixelSpacing1',
       'BitsAllocated', 'BitsStored', 'HighBit', 'PixelRepresentation',
       'LossyImageCompression', 'LossyImageCompressionMethod', 'fname',
       'img_min', 'img_max', 'img_mean', 'img_std', 'img_pct_window'],
      dtype='object')


dicom_train.sample(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


len(dicom_train)

26684


train_meta_df.head(10).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


print("length of train labels, class dataframe",len(train_meta_df))
print("length of metadata dataframe...........",len(dicom_train))

length of train labels, class dataframe 30227
length of metadata dataframe........... 26684


# Merge the metadata with training data on PatientID / patientId
dicom_metadata_final = pd.merge(train_meta_df, dicom_train, left_on='patientId', right_on='PatientID')


len(dicom_metadata_final)

30227


dicom_metadata_final.duplicated().sum()

0


dicom_metadata_final = dicom_metadata_final.drop(columns = 'PatientID')


dicom_metadata_final.sample(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


# Save the merged metadata file to a csv file for future reference
#dicom_metadata_final.to_csv('dicom_metadata_final.csv')
dicom_metadata_final.shape

(30227, 49)


show_images(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})

<Figure size 640x480 with 0 Axes>


plt.show()


import random

sample_data1 = random.sample(list(Lung_Opacity_Unique), 5)
sample_data2 = random.sample(list(Normal_Unique), 5)
sample_data3 = random.sample(list(Not_Normal_Unique), 5)

samples = sample_data1+sample_data2+sample_data3

fig, ax = plt.subplots(3,5)
fig.set_figheight(20)
fig.set_figwidth(20)
i = 0
j = 0

for loop_1 in samples:
    pat_id_1 = loop_1
    no_ind = train_meta_df[train_meta_df['patientId'] == pat_id_1].index.values
    l_ind = len(no_ind)
    fname = pat_id_1+".dcm"
    image_path = "stage_2_train_images/"+fname
    ds = dicom.dcmread(image_path)
    img = ds.pixel_array
    
    X = 0
    Y = 0
    width = 0
    height = 0
    rect = []

    ax[i][j].imshow(img, cmap=plt.cm.bone)
    for k in range(l_ind):
        X = train_meta_df.iloc[no_ind[k],1]
        Y = train_meta_df.iloc[no_ind[k],2]
        width = train_meta_df.iloc[no_ind[k],3]
        height = train_meta_df.iloc[no_ind[k],4]
        title = train_meta_df.iloc[no_ind[k],6]
        rect.append(patches.Rectangle((X, Y), width, height,linewidth = 3,edgecolor = 'r',facecolor = 'none'))
        ax[i][j].add_patch(rect[k])
        ax[i][j].set_title(title)
    j = j+1
    if j == 5:
        i = i+1
        j = 0
plt.figure().clear()
plt.show()

<Figure size 640x480 with 0 Axes>


fig, ax = plt.subplots(2,2)
fig.set_figheight(12)
fig.set_figwidth(12)

# UNIVARIATE ANALYSIS
#1. CLASS - NORMAL, LUNG OPACITY, NO LUNG OPACITY / NOT NORMAL
my_labels1 = dicom_metadata_final['class'].unique()
colors1 = ['#ff9999','#66b3ff','#ffcc99']
my_explode1 = [0.2,0,0]
ax[0][0].pie(dicom_metadata_final['class'].value_counts(sort = False),startangle=120,autopct='%1.1f%%',
        colors = colors1,explode = my_explode1,labels = my_labels1)
ax[0][0].set_title("Class Pie Chart\n" + "Normal or Lung Opacity or No Lung Opacity / Not Normal", bbox={'facecolor':'0.8', 'pad':5})
ax[0][0].legend(labels = my_labels1, loc="best")
ax[0][0].axis('equal')

#2. TARGET - 0 - NO PNEUMONIA / 1 - PNEUMONIA
my_labels2 = dicom_metadata_final['Target'].unique()
colors2 = ['#D8BFD8','#ECFFDC']
my_explode2 = [0,0.2]
ax[0][1].pie(dicom_metadata_final['Target'].value_counts(sort = False),startangle=225,autopct='%1.1f%%',
        labels = my_labels2,colors = colors2,explode = my_explode2)
ax[0][1].set_title("Target Pie Chart\n" + "1 - Pneumonia / 0 - No Pneumonia", bbox={'facecolor':'0.8', 'pad':5})
ax[0][1].legend(labels = my_labels2, loc="best")
ax[0][1].axis('equal')

#3. PATIENT SEX - M - MALE / F - FEMALE
my_labels3 = dicom_metadata_final['PatientSex'].unique()
colors3 = ['#66b3ff','#ff9999']
my_explode3 = [0,0.2]
ax[1][0].pie(dicom_metadata_final['PatientSex'].value_counts(sort = False),startangle=90,autopct='%1.1f%%',
        labels = my_labels3,colors = colors3,explode = my_explode3)
ax[1][0].set_title("Patient Sex Pie Chart\n" + "Whether Patient is Male - M or Female - F", bbox={'facecolor':'0.8', 'pad':5})
ax[1][0].legend(labels = my_labels3, loc="best")
ax[1][0].axis('equal')

#4. VIEW POSITION - PA - POSTERIOR TO ANTERIOR / AP - ANTERIOR TO POSTERIOR
my_labels4 = dicom_metadata_final['ViewPosition'].unique()
colors4 = ['#C1E1C1','#FAC898']
my_explode4 = [0,0.2]
ax[1][1].pie(dicom_metadata_final['ViewPosition'].value_counts(sort = False),startangle=90,autopct='%1.1f%%',
        labels = my_labels4,colors = colors4,explode = my_explode4)
ax[1][1].set_title("View Position Pie Chart\n" + "PA - Posterior to Anterior / AP - Anterior to Posterior", bbox={'facecolor':'0.8', 'pad':5})
ax[1][1].legend(labels = my_labels4, loc="best")
ax[1][1].axis('equal')

fig.tight_layout()
plt.show()


# CREATE A NEW COLUMN "AGE GROUP" AND ADD TO dicom_metadata_df

# ASSIGN THE VALUE BASED ON PATIENT AGE. FOR EX: 1-20, 21-40, 41-60, 61-80, 81-100, >100

AGE = []

dicom_metadata_final['PatientAge'] = dicom_metadata_final['PatientAge'].astype("int64")

for i in np.arange(0, dicom_metadata_final['PatientAge'].value_counts().sum(),1):
    if (dicom_metadata_final['PatientAge'][i] > 0) and (dicom_metadata_final['PatientAge'][i] <= 20):
        AGE.append('1-20')
    elif (dicom_metadata_final['PatientAge'][i] > 20) and (dicom_metadata_final['PatientAge'][i] <= 40):
        AGE.append('21-40')
    elif (dicom_metadata_final['PatientAge'][i] > 40) and (dicom_metadata_final['PatientAge'][i] <= 60):
        AGE.append('41-60')
    elif (dicom_metadata_final['PatientAge'][i] > 60) and (dicom_metadata_final['PatientAge'][i] <= 80):
        AGE.append('61-80')
    elif (dicom_metadata_final['PatientAge'][i] > 80) and (dicom_metadata_final['PatientAge'][i] <= 100):
        AGE.append('81-100')
    else :
        AGE.append('>100')
        
AGE = pd.Series(AGE)

dicom_metadata_final['AGE_GROUP'] = AGE.values



# AGE GROUP - PATIENT AGE GROUP - [1-20, 21-40, 41-60, 61-80, 81-100, >100]

sns.histplot(data = dicom_metadata_final, x = "AGE_GROUP",multiple="dodge", shrink=.8,palette = "Spectral")

plt.title("Patient Age Pie Chart\n" + "Shows Age of patient", bbox={'facecolor':'0.8', 'pad':5})

plt.show()


fig, ax = plt.subplots(2,3)
fig.set_figheight(12)
fig.set_figwidth(12)

# BI-VARIATE ANALYSIS

#1. PATIENT SEX AND CLASS
ax[0][0].set_title("Patient Sex vs CLass" + "\n............................................", bbox={'facecolor':'0.8', 'pad':5})
sns.countplot(data = dicom_metadata_final, x = "PatientSex",hue = "class", edgecolor = ".6",palette = "Spectral",ax = ax[0][0])

#2. PATIENT SEX AND TARGET
sns.countplot(data = dicom_metadata_final, x = "PatientSex",hue = "Target", edgecolor = ".6",palette = "Spectral",ax = ax[0][1])
ax[0][1].set_title("Patient Sex vs Target" + "\n............................................", bbox={'facecolor':'0.8', 'pad':5})

#3. PATIENT AGE GROUP AND CLASS
sns.countplot(data = dicom_metadata_final, x = "AGE_GROUP",hue = "class", edgecolor = ".6",palette = "Spectral",ax = ax[0][2])
ax[0][2].set_title("Patient Age vs Class\n"  + "\n............................................", bbox={'facecolor':'0.8', 'pad':5})

#4. PATIENT AGE GROUP AND TARGET
sns.countplot(data = dicom_metadata_final, x = "AGE_GROUP",hue = "Target", edgecolor = ".6",palette = "Spectral",ax = ax[1][0])
ax[1][0].set_title("Patient Age vs Target\n" + "\n............................................", bbox={'facecolor':'0.8', 'pad':5})

#5. VIEW POSITION AND CLASS
sns.countplot(data = dicom_metadata_final, x = "ViewPosition",hue = "class", edgecolor = ".6",palette = "Spectral",ax = ax[1][1])
ax[1][1].set_title("View Position vs Class\n" + "\n............................................", bbox={'facecolor':'0.8', 'pad':5})

#6. VIEW POSITION AND TARGET
sns.countplot(data = dicom_metadata_final, x = "ViewPosition",hue = "Target", edgecolor = ".6",palette = "Spectral",ax = ax[1][2])
ax[1][2].set_title("View Position vs Target\n" + "\n............................................", bbox={'facecolor':'0.8', 'pad':5})

fig.tight_layout()
plt.show()


#6. Class & TARGET

fig, ax = plt.subplots(1,1)
fig.set_figheight(6)
fig.set_figwidth(8)
sns.countplot(data = dicom_metadata_final, x = "Target", hue = "class", edgecolor = ".6",palette = "Spectral",ax=ax)
ax.set_title("Class vs Target\n" + "\n............................................", bbox={'facecolor':'0.8', 'pad':5})
fig.tight_layout()
plt.show()


dicom_metadata_final.describe(include='object').transpose().style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


dicom_metadata_final.describe().transpose().style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


dicom_required_df = dicom_metadata_final[["patientId","x","y","width","height","Target","class","SOPInstanceUID",
                                          "PatientSex","PatientAge","ViewPosition","StudyInstanceUID","SeriesInstanceUID","PixelSpacing",
                                          "PixelSpacing1","img_min","img_max","img_mean","img_std","img_pct_window"]]


dicom_required_df.dtypes

patientId             object
x                    float64
y                    float64
width                float64
height               float64
Target                 int64
class                 object
SOPInstanceUID        object
PatientSex            object
PatientAge             int64
ViewPosition          object
StudyInstanceUID      object
SeriesInstanceUID     object
PixelSpacing         float64
PixelSpacing1        float64
img_min                int64
img_max                int64
img_mean             float64
img_std              float64
img_pct_window       float64
dtype: object


print("dicom_metadata_final shape",dicom_metadata_final.shape)
print("dicom_required_df shape",dicom_required_df.shape)

dicom_metadata_final shape (30227, 49)
dicom_required_df shape (30227, 20)


dicom_eda_df = dicom_required_df[["patientId", "Target", "class", "PatientSex", "PatientAge", "ViewPosition"]]


dicom_eda_df.dtypes

patientId       object
Target           int64
class           object
PatientSex      object
PatientAge       int64
ViewPosition    object
dtype: object


fig, ax = plt.subplots(1,1,figsize=(7,7))
target_sample = dicom_required_df.sample(2000)
target_sample['xc'] = target_sample['x'] + target_sample['width'] / 2
target_sample['yc'] = target_sample['y'] + target_sample['height'] / 2
plt.title("Centers of Lung Opacity rectangles (red) over rectangles (cyan)\nSample size: 2000")
target_sample.plot.scatter(x='xc', y='yc', xlim=(0,1024), ylim=(0,1024), ax=ax, alpha=0.8, marker=".", color="red")
for i, crt_sample in target_sample.iterrows():
    ax.add_patch(patches.Rectangle(xy=(crt_sample['x'], crt_sample['y']),
                width=crt_sample['width'],height=crt_sample['height'],alpha=3.5e-3, color="cyan"))
plt.show()


# MULTI VARIATE ANALYSIS

#1. CORRELATION PLOT ON dicom_required_df

def CORR_PLOT(df):
    plt.figure(figsize = (16,10))
    df.corr()['Target'].sort_values(ascending = False).plot(kind = 'bar', figsize = (20,5))
    plt.show()

CORR_PLOT(dicom_required_df)


#2. BOX PLOT ON dicom_metadata_df

dicom_required_df.boxplot(figsize=(35,15))
plt.title("Box Plot on Metadata\n" + "............................................", bbox={'facecolor':'0.8', 'pad':5})
plt.show()


#3. HEAT MAP

plt.figure(figsize = (20,20))
sns.heatmap(dicom_required_df.drop(columns = 'Target').corr(), annot = True, linewidths = 1, square = True, cmap="YlOrRd")
plt.title("Heat Map on Metadata\n" + "............................................", bbox={'facecolor':'0.8', 'pad':5})
plt.show()


# Reset Index of the DF before proceedig for bounding box
train_Lung_Opacity = train_Lung_Opacity.reset_index(drop = True)

def bound_box(passed_sample):
    # Take a random sample of 5 images and print bounding box patch on images
    sample_data = random.sample(list(Lung_Opacity_Unique), passed_sample)
    if passed_sample/5 == 0:
        z = math.ceil(passed_sample/5)
    else:
        z = math.ceil((passed_sample)/5)

    bounding_box_data = pd.DataFrame()

    fig, ax = plt.subplots(z,5,constrained_layout=True)
    fig.set_figheight(20)
    fig.set_figwidth(20)
    
    i = 0
    j = 0

    for loop_1 in sample_data:
        pat_id_1 = loop_1

        no_ind = train_Lung_Opacity[train_Lung_Opacity['patientId'] == pat_id_1].index.values
        l_ind = len(no_ind)

        fname = pat_id_1+".dcm"
        image_path = "Training/Lung Opacity/"+fname
        ds = dicom.dcmread(image_path)
        img = ds.pixel_array

        X = 0
        Y = 0
        width = 0
        height = 0
        rect = []

        for k in range(l_ind):
            X = train_Lung_Opacity.iloc[no_ind[k],1]
            Y = train_Lung_Opacity.iloc[no_ind[k],2]
            width = train_Lung_Opacity.iloc[no_ind[k],3]
            height = train_Lung_Opacity.iloc[no_ind[k],4]
            bounding_box_data = bounding_box_data.append(pd.Series([pat_id_1, l_ind,X,Y,width,height]), ignore_index = True)
            rect.append(patches.Rectangle((X, Y), width, height,linewidth = 3,edgecolor = 'r',facecolor = 'none'))
            ax[i][j].imshow(img, cmap=plt.cm.bone)
            ax[i][j].add_patch(rect[k])

        j = j+1
        if j == 5:
            i = i+1
            j = 0
    plt.figure().clear()
    plt.show()
    bounding_box_data.columns = ['Image Name','No. Of Bounding Boxes','X','Y','Width','Height']
    return(bounding_box_data)

# Add columns to the bounding_box_data
# bounding_box_data.columns = ['Patient_ID','No. of Bounding Boxes','X','Y','Width','Height']
# Save the sample bounding_box_data to a csv file for reference
# bounding_box_data.to_csv(cap_path+'bounding_box_data.csv')


bound_box(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()

<Figure size 640x480 with 0 Axes>


Images_df.sample(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


Images_df.dtypes

Folder                  object
Image_Class             object
Image_file              object
Full_filename           object
Image_Class_Category     int32
Image_Target             int64
dtype: object


Images_sample1_a = Images_df[Images_df["Image_Target"] == 0]
Images_sample2_a = Images_df[Images_df["Image_Target"] == 1]


print("No Pneumonia",len(Images_sample1_a))
print("\nImages Pneumonia",len(Images_sample2_a))

No Pneumonia 20672

Images Pneumonia 6012


sample1_a = Images_sample1_a.sample(n = 3000)
sample2_a = Images_sample2_a.sample(n = 3000)


sams_1a = [sample1_a, sample2_a]


result_1a = pd.concat(sams_1a)
print(result_1a.shape)
print(result_1a["Image_Target"].value_counts())

(6000, 6)
0    3000
1    3000
Name: Image_Target, dtype: int64


Images_6000_Target = result_1a.copy()


img_rows=224
img_cols=224
dim = (img_rows, img_cols)

X_target = []
brk = 0
i = 1 # initialisation
for img in tqdm(Images_6000_Target["Full_filename"].values): 
    
    ds_3 = dicom.dcmread(img)
    img_3 = ds_3.pixel_array
    rgb = apply_color_lut(img_3, palette='PET')
    train_img = rgb
    try:
        train_img_resize = cv2.resize(train_img, dim, interpolation=cv2.INTER_LINEAR)
    except:
        brk +=1
        print("breaking out for",img)
        break
    height_2, width_2, layers = train_img_resize.shape
    size=(width_2,height_2)
    X_target.append(train_img_resize)
    i += 1

100%|██████████████████████████████████████████████████████████████████████████████| 6000/6000 [02:41<00:00, 37.26it/s]


fileName = "Images_X_3_target_6000.pkl"
fileObject = open(fileName, 'wb')

pkl.dump(X_target, fileObject)
fileObject.close()


Images_6000_Target.to_pickle("Images_6000_Target.pkl") # pickling the proportionate target from 6000 Images dataframe


image_pickle = open ("Images_6000_Target.pkl", "rb")
Images_6000_Target = pkl.load(image_pickle)
Images_6000_Target.sample(10).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


image_X_3 = open ("Images_X_3_target_6000.pkl", "rb")
X = pkl.load(image_X_3)
X[0:2]

[array([[[  0,  39,  38],
         [  0,  35,  35],
         [  0,  34,  33],
         ...,
         [  0,  40,  40],
         [  0,  45,  44],
         [  0,  34,  33]],
 
        [[  0,  36,  35],
         [  0,  34,  33],
         [  0,  31,  30],
         ...,
         [  0,  54,  53],
         [  0,  58,  56],
         [  0,  85,  83]],
 
        [[  0,  34,  33],
         [  0,  32,  32],
         [  0,  30,  29],
         ...,
         [  0,  25,  24],
         [  0,  17,  17],
         [  0,  20,  19]],
 
        ...,
 
        [[  0,  53,  52],
         [  0,  58,  57],
         [  0,  48,  47],
         ...,
         [ 44,  82, 170],
         [ 20, 107, 146],
         [  0, 118, 116]],
 
        [[  0,  52,  51],
         [  0,  55,  54],
         [  0,  49,  48],
         ...,
         [ 51,  76, 177],
         [ 24, 102, 150],
         [  1, 126, 127]],
 
        [[  0,  55,  53],
         [  0,  48,  47],
         [  0,  44,  43],
         ...,
         [ 57,  70, 183],
         [ 28,  99, 154],
         [  1, 126, 126]]], dtype=uint8),
 array([[[  0,   3,   2],
         [  0,   7,   6],
         [  0,   3,   2],
         ...,
         [  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0]],
 
        [[  0,   3,   2],
         [  0,   7,   6],
         [  0,   6,   5],
         ...,
         [  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0]],
 
        [[  0,   3,   2],
         [  0,   9,   8],
         [  0,   6,   5],
         ...,
         [  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0]],
 
        ...,
 
        [[  0,  17,  16],
         [146,  17, 221],
         [ 68,  60, 194],
         ...,
         [  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0]],
 
        [[  0,  17,  16],
         [119,   8, 246],
         [ 99,  28, 226],
         ...,
         [  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0]],
 
        [[  0,  15,  14],
         [ 84,  43, 210],
         [127,   6, 247],
         ...,
         [  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0]]], dtype=uint8)]


X_tar = np.array(X)


X_tar.shape

(6000, 224, 224, 3)


print(X_tar.max())
print(X_tar.min())

255
0


y1_tar = Images_6000_Target['Image_Target']


y1_tar.value_counts()

0    3000
1    3000
Name: Image_Target, dtype: int64


y1_tar_cat = to_categorical(y1_tar, num_classes=2)

print("Shape of y1_tar_cat:", y1_tar_cat.shape)

Shape of y1_tar_cat: (6000, 2)


y1_tar_cat[0:10]

array([[1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.]], dtype=float32)


print("X_tar shape",X_tar.shape)
print("y1_tar_cat shape",y1_tar_cat.shape)

X_tar shape (6000, 224, 224, 3)
y1_tar_cat shape (6000, 2)


X_train, X_test, y_train, y_test = train_test_split(X_tar, y1_tar_cat, test_size=.20, stratify=y1_tar_cat, random_state=1)  # 80% Training and 20% Testing


print("X_train",X_train.shape)
print("y_train",y_train.shape)  

print("X_test",X_test.shape)
print("y_test",y_test.shape)

X_train (4800, 224, 224, 3)
y_train (4800, 2)
X_test (1200, 224, 224, 3)
y_test (1200, 2)


X_train1, X_val, y_train1, y_val = train_test_split(X_train, y_train, test_size=.20, stratify=y_train, random_state=1)  # 80% Training and 20% Testing


print("X_train1",X_train1.shape)
print("y_train1",y_train1.shape)  

print("X_val",X_val.shape)
print("y_val",y_val.shape)

X_train1 (3840, 224, 224, 3)
y_train1 (3840, 2)
X_val (960, 224, 224, 3)
y_val (960, 2)


print(X_val[0])
print(y_val[0])

[[[  0 107 105]
  [  0  76  74]
  [  0  50  49]
  ...
  [  0  84  82]
  [  0  90  88]
  [  0 108 106]]

 [[  0 107 105]
  [  0  75  74]
  [  0  48  47]
  ...
  [  0  46  45]
  [  0  47  46]
  [  0  63  61]]

 [[  0 107 104]
  [  0  75  73]
  [  0  46  45]
  ...
  [  0  16  15]
  [  0  20  18]
  [  0  33  32]]

 ...

 [[  0  18  17]
  [  0   6   5]
  [  0  10   9]
  ...
  [  0  24  23]
  [  0  45  44]
  [  0  78  75]]

 [[  0  20  19]
  [  0  13  12]
  [  0  18  17]
  ...
  [  0  28  27]
  [  0  47  46]
  [  0  78  76]]

 [[  0  28  27]
  [  0  24  23]
  [  0  30  29]
  ...
  [  0  30  29]
  [  0  48  47]
  [  0  79  77]]]
[1. 0.]


tf.keras.backend.clear_session()

# Initialize the model
model = Sequential()

# Add a Convolutional Layer with 32 filters of size 7X7, strides(2,2) and activation function as 'relu' 
model.add(Conv2D(filters=32, kernel_size=7, strides=(2, 2), activation="relu", input_shape=(224, 224, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())

# Add a Convolutional Layer with 64 filters of size 5X5, strides(2,2) and activation function as 'relu' 
model.add(Conv2D(filters=64, kernel_size=5, strides=(1, 1), activation="relu"))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))

# model.add(Dropout(rate=0.3))

# model.add(GlobalMaxPooling2D())

# Apply Dropout with 0.2 probability 
# model.add(Dropout(rate=0.2))

# Add a Convolutional Layer with 64 filters of size 3X3 and activation function as 'relu' 
# model.add(Conv2D(filters=64, kernel_size=3, strides=(2, 2), activation="relu"))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(BatchNormalization())

# Flatten the layer
model.add(Flatten())

# Add Fully Connected Layer with 128 units and activation function as 'relu'
model.add(Dense(128, activation="relu"))
model.add(Dense(64, activation="relu"))

# model.add(Dropout(rate=0.3))

# model.add(BatchNormalization())

# model.add(Dense(128, activation="relu"))

#Add Fully Connected Layer with 3 units and activation function as 'softmax'
model.add(Dense(2, activation="sigmoid"))


optimizer = Adam(learning_rate=0.01)
model.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 109, 109, 32)      4736      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 54, 54, 32)       0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 54, 54, 32)       128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 50, 50, 64)        51264     
                                                                 
 batch_normalization_1 (Batc  (None, 50, 50, 64)       256       
 hNormalization)                                                 
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 25, 25, 64)       0         
 2D)                                                             
                                                                 
 flatten (Flatten)           (None, 40000)             0         
                                                                 
 dense (Dense)               (None, 128)               5120128   
                                                                 
 dense_1 (Dense)             (None, 64)                8256      
                                                                 
 dense_2 (Dense)             (None, 2)                 130       
                                                                 
=================================================================
Total params: 5,184,898
Trainable params: 5,184,706
Non-trainable params: 192
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history = model.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 34s 550ms/step - loss: 4.3237 - accuracy: 0.6383 - val_loss: 14.4939 - val_accuracy: 0.5354
Epoch 2/10
60/60 [==============================] - 37s 609ms/step - loss: 0.6385 - accuracy: 0.7107 - val_loss: 1.2404 - val_accuracy: 0.6750
Epoch 3/10
60/60 [==============================] - 36s 603ms/step - loss: 0.5882 - accuracy: 0.7247 - val_loss: 0.7027 - val_accuracy: 0.6833
Epoch 4/10
60/60 [==============================] - 36s 600ms/step - loss: 0.5466 - accuracy: 0.7294 - val_loss: 0.6329 - val_accuracy: 0.7052
Epoch 5/10
60/60 [==============================] - 36s 596ms/step - loss: 0.5267 - accuracy: 0.7479 - val_loss: 0.6261 - val_accuracy: 0.7188
Epoch 6/10
60/60 [==============================] - 36s 592ms/step - loss: 0.5200 - accuracy: 0.7443 - val_loss: 0.6133 - val_accuracy: 0.7292
Epoch 7/10
60/60 [==============================] - 36s 596ms/step - loss: 0.5267 - accuracy: 0.7487 - val_loss: 0.6255 - val_accuracy: 0.7167
Epoch 8/10
60/60 [==============================] - 36s 603ms/step - loss: 0.5303 - accuracy: 0.7495 - val_loss: 0.5592 - val_accuracy: 0.7406
Epoch 9/10
60/60 [==============================] - 36s 599ms/step - loss: 0.4858 - accuracy: 0.7711 - val_loss: 0.6025 - val_accuracy: 0.7260
Epoch 10/10
60/60 [==============================] - 35s 590ms/step - loss: 0.4649 - accuracy: 0.7828 - val_loss: 0.6397 - val_accuracy: 0.7344


plt.figure(figsize=(6,4))
plt.plot(history.history['loss'], label='train')
plt.plot(history.history['val_loss'], label='test')
plt.legend()
plt.show()


plt.figure(figsize=(6,4))
plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='test')
plt.legend()
plt.show()


tf.keras.backend.clear_session()

# second model
model_2 = Sequential()

model_2.add(Conv2D(filters=32, kernel_size=7, strides=(2, 2), activation="relu", input_shape=(224, 224, 3)))
model_2.add(MaxPooling2D(pool_size=(2, 2)))
model_2.add(BatchNormalization())

model_2.add(Conv2D(filters=64, kernel_size=5, strides=(1, 1), activation="relu"))
model_2.add(MaxPooling2D(pool_size=(2, 2)))
model_2.add(BatchNormalization())

#model_2.add(Dropout(rate=0.3))

model_2.add(Conv2D(filters=128, kernel_size=3, strides=(1, 1), activation="relu"))
model_2.add(MaxPooling2D(pool_size=(2, 2)))
model_2.add(BatchNormalization())

model_2.add(Flatten())

model_2.add(Dense(128, activation="relu"))
# model_2.add(BatchNormalization())

model_2.add(Dense(84, activation="relu"))
# model_2.add(BatchNormalization())
model_2.add(Dense(42, activation="relu"))

model_2.add(Dense(2, activation="sigmoid"))


optimizer = Adam(learning_rate=0.001)
model_2.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model_2.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 109, 109, 32)      4736      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 54, 54, 32)       0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 54, 54, 32)       128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 50, 50, 64)        51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 25, 25, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 25, 25, 64)       256       
 hNormalization)                                                 
                                                                 
 conv2d_2 (Conv2D)           (None, 23, 23, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 11, 11, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 11, 11, 128)      512       
 hNormalization)                                                 
                                                                 
 flatten (Flatten)           (None, 15488)             0         
                                                                 
 dense (Dense)               (None, 128)               1982592   
                                                                 
 dense_1 (Dense)             (None, 84)                10836     
                                                                 
 dense_2 (Dense)             (None, 42)                3570      
                                                                 
 dense_3 (Dense)             (None, 2)                 86        
                                                                 
=================================================================
Total params: 2,127,836
Trainable params: 2,127,388
Non-trainable params: 448
_________________________________________________________________


batch_size = 64
nb_epochs = 20
history_2 = model_2.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.0001)])

Epoch 1/20
60/60 [==============================] - 35s 576ms/step - loss: 0.6190 - accuracy: 0.6997 - val_loss: 1.3135 - val_accuracy: 0.5698
Epoch 2/20
60/60 [==============================] - 37s 610ms/step - loss: 0.5663 - accuracy: 0.7219 - val_loss: 0.6272 - val_accuracy: 0.6802
Epoch 3/20
60/60 [==============================] - 37s 611ms/step - loss: 0.5180 - accuracy: 0.7437 - val_loss: 0.5994 - val_accuracy: 0.7271
Epoch 4/20
60/60 [==============================] - 37s 609ms/step - loss: 0.4965 - accuracy: 0.7594 - val_loss: 0.6675 - val_accuracy: 0.6896
Epoch 5/20
60/60 [==============================] - 36s 607ms/step - loss: 0.4684 - accuracy: 0.7729 - val_loss: 0.6723 - val_accuracy: 0.6854
Epoch 6/20
60/60 [==============================] - 37s 612ms/step - loss: 0.4272 - accuracy: 0.7964 - val_loss: 0.7234 - val_accuracy: 0.6729


plt.figure(figsize=(6,4))
plt.plot(history_2.history['loss'], label='train')
plt.plot(history_2.history['val_loss'], label='test')
plt.legend()
plt.show()


plt.figure(figsize=(6,4))
plt.plot(history_2.history['accuracy'], label='train')
plt.plot(history_2.history['val_accuracy'], label='test')
plt.legend()
plt.show()


tf.keras.backend.clear_session()

# second model
model_2a = Sequential()

model_2a.add(Conv2D(filters=32, kernel_size=5, strides=(1, 1), activation="relu", input_shape=(224, 224, 3)))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Conv2D(filters=64, kernel_size=5, strides=(1, 1), activation="relu"))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Conv2D(filters=128, kernel_size=3, strides=(1, 1), activation="relu"))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Dropout(rate=0.2))

model_2a.add(Conv2D(filters=256, kernel_size=3, strides=(1, 1), activation="relu"))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Dropout(rate=0.2))

#model_2a.add(Conv2D(filters=512, kernel_size=3, strides=(1, 1), activation="relu"))
#model_2a.add(MaxPooling2D(pool_size=(2, 2)))
#model_2a.add(BatchNormalization())

model_2a.add(Flatten())

#model_2a.add(Dense(512, activation="relu"))
# model_2a.add(Dropout(rate=0.7))

model_2a.add(Dense(256, activation="relu"))
model_2a.add(Dropout(rate=0.25))

model_2a.add(Dense(128, activation="relu"))
# model_2a.add(Dropout(rate=0.3))

model_2a.add(Dense(64, activation="relu"))

model_2a.add(Dense(32, activation="relu"))

model_2a.add(Dense(2, activation="sigmoid"))


optimizer = Adam(learning_rate=0.0001)
model_2a.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"]) 
model_2a.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 220, 220, 32)      2432      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 110, 110, 32)     0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 110, 110, 32)     128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 106, 106, 64)      51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 53, 53, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 53, 53, 64)       256       
 hNormalization)                                                 
                                                                 
 conv2d_2 (Conv2D)           (None, 51, 51, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 25, 25, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 25, 25, 128)      512       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 25, 25, 128)       0         
                                                                 
 conv2d_3 (Conv2D)           (None, 23, 23, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 11, 11, 256)      0         
 2D)                                                             
                                                                 
 batch_normalization_3 (Batc  (None, 11, 11, 256)      1024      
 hNormalization)                                                 
                                                                 
 dropout_1 (Dropout)         (None, 11, 11, 256)       0         
                                                                 
 flatten (Flatten)           (None, 30976)             0         
                                                                 
 dense (Dense)               (None, 256)               7930112   
                                                                 
 dropout_2 (Dropout)         (None, 256)               0         
                                                                 
 dense_1 (Dense)             (None, 128)               32896     
                                                                 
 dense_2 (Dense)             (None, 64)                8256      
                                                                 
 dense_3 (Dense)             (None, 32)                2080      
                                                                 
 dense_4 (Dense)             (None, 2)                 66        
                                                                 
=================================================================
Total params: 8,398,050
Trainable params: 8,397,090
Non-trainable params: 960
_________________________________________________________________


batch_size = 54
nb_epochs = 20
history_2a = model_2a.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.01)])

Epoch 1/20
72/72 [==============================] - 174s 2s/step - loss: 0.6146 - accuracy: 0.6885 - val_loss: 0.6254 - val_accuracy: 0.6854
Epoch 2/20
72/72 [==============================] - 176s 2s/step - loss: 0.5556 - accuracy: 0.7232 - val_loss: 0.5876 - val_accuracy: 0.6875
Epoch 3/20
72/72 [==============================] - 175s 2s/step - loss: 0.5299 - accuracy: 0.7414 - val_loss: 0.5784 - val_accuracy: 0.6969
Epoch 4/20
72/72 [==============================] - 176s 2s/step - loss: 0.4800 - accuracy: 0.7680 - val_loss: 0.5746 - val_accuracy: 0.7240
Epoch 5/20
72/72 [==============================] - 176s 2s/step - loss: 0.4530 - accuracy: 0.7831 - val_loss: 0.5635 - val_accuracy: 0.7333
Epoch 6/20
72/72 [==============================] - 176s 2s/step - loss: 0.4168 - accuracy: 0.8057 - val_loss: 0.6096 - val_accuracy: 0.7104
Epoch 7/20
72/72 [==============================] - 176s 2s/step - loss: 0.3621 - accuracy: 0.8320 - val_loss: 0.6227 - val_accuracy: 0.7031
Epoch 8/20
72/72 [==============================] - 175s 2s/step - loss: 0.3344 - accuracy: 0.8510 - val_loss: 0.6787 - val_accuracy: 0.7063


plt.figure(figsize=(6,4))
plt.plot(history_2a.history['loss'], label='train')
plt.plot(history_2a.history['val_loss'], label='test')
plt.legend()
plt.show()


plt.figure(figsize=(6,4))
plt.plot(history_2a.history['accuracy'], label='train')
plt.plot(history_2a.history['val_accuracy'], label='test')
plt.legend()
plt.show()


tf.keras.backend.clear_session()

# second model
model_3 = Sequential()

model_3.add(Conv2D(filters=32, kernel_size=5, strides=(1, 1), activation="relu", input_shape=(224, 224, 3)))
model_3.add(MaxPooling2D(pool_size=(2, 2)))

model_3.add(Conv2D(filters=64, kernel_size=5, strides=(1, 1), kernel_initializer='normal', activation="relu"))
model_3.add(MaxPooling2D(pool_size=(2, 2)))

model_3.add(Conv2D(filters=128, kernel_size=3, strides=(1, 1), kernel_initializer='normal', activation="relu"))
model_3.add(MaxPooling2D(pool_size=(2, 2)))

model_3.add(BatchNormalization())

model_3.add(Conv2D(filters=256, kernel_size=3, strides=(1, 1), kernel_initializer='normal', activation="relu"))
model_3.add(MaxPooling2D(pool_size=(2, 2)))

model_3.add(Dropout(rate=0.3))

model_3.add(Flatten())

model_3.add(Dense(128, kernel_initializer='normal', activation="relu"))

model_3.add(Dense(84, kernel_initializer='normal', activation="relu"))

model_3.add(Dense(42, kernel_initializer='normal', activation="relu"))

model_3.add(Dense(2, activation="sigmoid"))


optimizer = Adam(learning_rate=0.0001)
model_3.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model_3.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 220, 220, 32)      2432      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 110, 110, 32)     0         
 )                                                               
                                                                 
 conv2d_1 (Conv2D)           (None, 106, 106, 64)      51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 53, 53, 64)       0         
 2D)                                                             
                                                                 
 conv2d_2 (Conv2D)           (None, 51, 51, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 25, 25, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization (BatchN  (None, 25, 25, 128)      512       
 ormalization)                                                   
                                                                 
 conv2d_3 (Conv2D)           (None, 23, 23, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 11, 11, 256)      0         
 2D)                                                             
                                                                 
 dropout (Dropout)           (None, 11, 11, 256)       0         
                                                                 
 flatten (Flatten)           (None, 30976)             0         
                                                                 
 dense (Dense)               (None, 128)               3965056   
                                                                 
 dense_1 (Dense)             (None, 84)                10836     
                                                                 
 dense_2 (Dense)             (None, 42)                3570      
                                                                 
 dense_3 (Dense)             (None, 2)                 86        
                                                                 
=================================================================
Total params: 4,402,780
Trainable params: 4,402,524
Non-trainable params: 256
_________________________________________________________________


batch_size = 64
nb_epochs = 20
history_3 = model_3.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.001)])

Epoch 1/20
60/60 [==============================] - 156s 3s/step - loss: 0.6527 - accuracy: 0.6594 - val_loss: 0.6083 - val_accuracy: 0.7031
Epoch 2/20
60/60 [==============================] - 160s 3s/step - loss: 0.5647 - accuracy: 0.7286 - val_loss: 0.5843 - val_accuracy: 0.7198
Epoch 3/20
60/60 [==============================] - 158s 3s/step - loss: 0.5501 - accuracy: 0.7260 - val_loss: 0.5555 - val_accuracy: 0.7385
Epoch 4/20
60/60 [==============================] - 158s 3s/step - loss: 0.5343 - accuracy: 0.7414 - val_loss: 0.5385 - val_accuracy: 0.7385
Epoch 5/20
60/60 [==============================] - 158s 3s/step - loss: 0.5088 - accuracy: 0.7625 - val_loss: 0.5368 - val_accuracy: 0.7375
Epoch 6/20
60/60 [==============================] - 158s 3s/step - loss: 0.4993 - accuracy: 0.7607 - val_loss: 0.5299 - val_accuracy: 0.7437
Epoch 7/20
60/60 [==============================] - 158s 3s/step - loss: 0.4814 - accuracy: 0.7734 - val_loss: 0.5503 - val_accuracy: 0.7281
Epoch 8/20
60/60 [==============================] - 159s 3s/step - loss: 0.4626 - accuracy: 0.7799 - val_loss: 0.5377 - val_accuracy: 0.7448
Epoch 9/20
60/60 [==============================] - 158s 3s/step - loss: 0.4446 - accuracy: 0.7969 - val_loss: 0.5479 - val_accuracy: 0.7260


plt.figure(figsize=(6,4))
plt.plot(history_3.history['loss'], label='train')
plt.plot(history_3.history['val_loss'], label='test')
plt.legend()
plt.show()


plt.figure(figsize=(6,4))
plt.plot(history_3.history['accuracy'], label='train')
plt.plot(history_3.history['val_accuracy'], label='test')
plt.legend()
plt.show()


plot_model(model,to_file="model_0_plot.png", show_shapes = True, show_layer_names = True)


plot_model(model_2,to_file="model_2_plot.png", show_shapes = True, show_layer_names = True)


plot_model(model_2a,to_file="model_2a_plot.png", show_shapes = True, show_layer_names = True)


plot_model(model_3,to_file="model_3_plot.png", show_shapes = True, show_layer_names = True)


# serialize model to JSON
model_json = model.to_json()
with open("model.json", "w") as json_file:
    json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model.h5")
print("Saved model to disk")

Saved model to disk


# serialize model to JSON
model_json = model.to_json()
with open("model_2.json", "w") as json_file:
    json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model_2.h5")
print("Saved model to disk")

Saved model to disk


# serialize model to JSON
model_json = model.to_json()
with open("model_2a.json", "w") as json_file:
    json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model_2a.h5")
print("Saved model to disk")

Saved model to disk


# serialize model to JSON
model_json = model.to_json()
with open("model_3.json", "w") as json_file:
    json_file.write(model_json)
# serialize weights to HDF5
model.save_weights("model_3.h5")
print("Saved model to disk")

Saved model to disk


# load json and create model

json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model.h5")
print("Loaded model from disk")

loaded_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
score = loaded_model.evaluate(X_test, y_test, verbose=0)
print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))

Loaded model from disk
accuracy: 71.25%


# load json and create model
json_file = open('model_2.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model_2.h5")
print("Loaded model from disk")

loaded_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
score = loaded_model.evaluate(X_test, y_test, verbose=0)
print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))

Loaded model from disk
accuracy: 71.25%


# load json and create model
json_file = open('model_2a.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model_2a.h5")
print("Loaded model from disk")

loaded_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
score = loaded_model.evaluate(X_test, y_test, verbose=0)
print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))

Loaded model from disk
accuracy: 71.25%


# load json and create model
json_file = open('model_3.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model_3.h5")
print("Loaded model from disk")

loaded_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
score = loaded_model.evaluate(X_test, y_test, verbose=0)
print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))

Loaded model from disk
accuracy: 71.25%


loss_1, accuracy_1 = model.evaluate(X_test,y_test)

38/38 [==============================] - 2s 47ms/step - loss: 0.6716 - accuracy: 0.7125


y_pred_1 = model.predict(X_test)

38/38 [==============================] - 2s 47ms/step


y_test_arg=np.argmax(y_test,axis=1)
Y_pred_1 = np.argmax(y_pred_1,axis=1)


RESULTS = pd.DataFrame()

RESULTS = RESULTS.append(pd.Series(["model",np.round((loss_1*100),2),np.round((accuracy_1*100),2)]),ignore_index=True)


cm = confusion_matrix(y_test_arg, Y_pred_1)

labels = ["0 - No Pneumonia", "1 - Pneumonia"]

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)

disp.plot()

plt.show()


print(classification_report(y_test_arg, Y_pred_1))

              precision    recall  f1-score   support

           0       0.70      0.75      0.72       600
           1       0.73      0.68      0.70       600

    accuracy                           0.71      1200
   macro avg       0.71      0.71      0.71      1200
weighted avg       0.71      0.71      0.71      1200


loss_2, accuracy_2 = model_2.evaluate(X_test,y_test)

38/38 [==============================] - 2s 47ms/step - loss: 0.7090 - accuracy: 0.6817


y_pred_2 = model_2.predict(X_test)

38/38 [==============================] - 2s 47ms/step


Y_pred_2 = np.argmax(y_pred_2,axis=1)


RESULTS = RESULTS.append(pd.Series(["model_2",np.round((loss_2*100),2),np.round((accuracy_2*100),2)]),ignore_index=True)


cm = confusion_matrix(y_test_arg, Y_pred_2)

labels = ["0 - No Pneumonia", "1 - Pneumonia"]

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)

disp.plot()

plt.show()


print(classification_report(y_test_arg, Y_pred_2))

              precision    recall  f1-score   support

           0       0.68      0.69      0.68       600
           1       0.68      0.68      0.68       600

    accuracy                           0.68      1200
   macro avg       0.68      0.68      0.68      1200
weighted avg       0.68      0.68      0.68      1200


loss_2a, accuracy_2a = model_2a.evaluate(X_test,y_test)

38/38 [==============================] - 8s 198ms/step - loss: 0.7499 - accuracy: 0.6775


y_pred_2a = model.predict(X_test)

38/38 [==============================] - 2s 47ms/step


Y_pred_2a = np.argmax(y_pred_2a,axis=1)


RESULTS = RESULTS.append(pd.Series(["model_2a",np.round((loss_2a*100),2),np.round((accuracy_2a*100),2)]),ignore_index=True)


cm = confusion_matrix(y_test_arg, Y_pred_2a)

labels = ["0 - No Pneumonia", "1 - Pneumonia"]

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)

disp.plot()

plt.show()


print(classification_report(y_test_arg, Y_pred_2a))

              precision    recall  f1-score   support

           0       0.70      0.75      0.72       600
           1       0.73      0.68      0.70       600

    accuracy                           0.71      1200
   macro avg       0.71      0.71      0.71      1200
weighted avg       0.71      0.71      0.71      1200


loss_3, accuracy_3 = model_3.evaluate(X_test,y_test)

38/38 [==============================] - 7s 176ms/step - loss: 0.5918 - accuracy: 0.6958


y_pred_3 = model.predict(X_test)

38/38 [==============================] - 2s 50ms/step


Y_pred_3 = np.argmax(y_pred_3,axis=1)


RESULTS = RESULTS.append(pd.Series(["model_3",np.round((loss_3*100),2),np.round((accuracy_3*100),2)]),ignore_index=True)


cm = confusion_matrix(y_test_arg, Y_pred_3)

labels = ["0 - No Pneumonia", "1 - Pneumonia"]

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)

disp.plot()

plt.show()


print(classification_report(y_test_arg, Y_pred_3))

              precision    recall  f1-score   support

           0       0.70      0.75      0.72       600
           1       0.73      0.68      0.70       600

    accuracy                           0.71      1200
   macro avg       0.71      0.71      0.71      1200
weighted avg       0.71      0.71      0.71      1200


RESULTS.columns = ['MODEL_NAME','LOSS','ACCURACY']

RESULTS.style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()


RESULTS.to_csv("base_model_results.csv", index = False)


print("X_test.shape",X_test.shape)
print("y_test.shape",y_test.shape)

X_test.shape (1200, 224, 224, 3)
y_test.shape (1200, 2)


import random

for i in range(1,6):
    rand_test = random.randint(0, 1200)
    print("random test sample...:",i)
    print("")
    print("     random test number",rand_test)
    print("")
    # print("   X_test",X_test[rand_test])
    print("    y_test",y_test[rand_test])
    print("    y_pred_1",y_pred_1[rand_test])
    print("    argmax",np.argmax(y_pred_1[rand_test]))
    print("    y_pred_2",y_pred_2[rand_test])
    print("    argmax",np.argmax(y_pred_2[rand_test]))
    print("    y_pred_2a",y_pred_2a[rand_test])
    print("    argmax",np.argmax(y_pred_2a[rand_test]))
    print("    y_pred_3",y_pred_3[rand_test])
    print("    argmax",np.argmax(y_pred_3[rand_test]))
    print("")

random test sample...: 1

     random test number 323

    y_test [1. 0.]
    y_pred_1 [0.9543633  0.01797388]
    argmax 0
    y_pred_2 [0.9403397  0.01519391]
    argmax 0
    y_pred_2a [0.9543633  0.01797388]
    argmax 0
    y_pred_3 [0.9543633  0.01797388]
    argmax 0

random test sample...: 2

     random test number 764

    y_test [0. 1.]
    y_pred_1 [0.91296834 0.00319313]
    argmax 0
    y_pred_2 [0.5173416  0.21540992]
    argmax 0
    y_pred_2a [0.91296834 0.00319313]
    argmax 0
    y_pred_3 [0.91296834 0.00319313]
    argmax 0

random test sample...: 3

     random test number 894

    y_test [1. 0.]
    y_pred_1 [0.9359591  0.00203102]
    argmax 0
    y_pred_2 [0.39366844 0.41483364]
    argmax 1
    y_pred_2a [0.9359591  0.00203102]
    argmax 0
    y_pred_3 [0.9359591  0.00203102]
    argmax 0

random test sample...: 4

     random test number 227

    y_test [1. 0.]
    y_pred_1 [0.904644   0.06956016]
    argmax 0
    y_pred_2 [0.7016851  0.21505149]
    argmax 0
    y_pred_2a [0.904644   0.06956016]
    argmax 0
    y_pred_3 [0.904644   0.06956016]
    argmax 0

random test sample...: 5

     random test number 272

    y_test [0. 1.]
    y_pred_1 [0.11444376 0.91253275]
    argmax 1
    y_pred_2 [0.7973255  0.13347106]
    argmax 0
    y_pred_2a [0.11444376 0.91253275]
    argmax 1
    y_pred_3 [0.11444376 0.91253275]
    argmax 1


print("No of image with No Pneumonia",len(Images_df[Images_df["Image_Target"] == 0]))
print("\nNo of Images with Pneumonia",len(Images_df[Images_df["Image_Target"] == 1]))

No of image with No Pneumonia 20672

No of Images with Pneumonia 6012


sample1_5000 = Images_sample1_a.sample(n = 5000)
sample2_5000 = Images_sample2_a.sample(n = 5000)

sams_5000 = [sample1_5000, sample2_5000]

result_5000 = pd.concat(sams_5000)
print('Shape of the Increased Training Data: ',result_5000.shape)
print('\nNo of Images in Target 0 and Target 1:\n',result_5000["Image_Target"].value_counts())

Shape of the Increased Training Data:  (10000, 6)

No of Images in Target 0 and Target 1:
 0    5000
1    5000
Name: Image_Target, dtype: int64


Images_10000_Target = result_5000.copy()

Images_10000_Target.dtypes

Folder                  object
Image_Class             object
Image_file              object
Full_filename           object
Image_Class_Category     int32
Image_Target             int64
dtype: object


img_rows=224
img_cols=224
dim = (img_rows, img_cols)

X_target = []
brk = 0
i = 1 # initialisation
for img in tqdm(Images_10000_Target["Full_filename"].values): 
    
    ds_3 = dicom.dcmread(img)
    img_3 = ds_3.pixel_array
    rgb = apply_color_lut(img_3, palette='PET')
    train_img = rgb
    try:
        train_img_resize = cv2.resize(train_img, dim, interpolation=cv2.INTER_LINEAR)
    except:
        brk +=1
        print("breaking out for",img_3)
        break
    height_2, width_2, layers = train_img_resize.shape
    size=(width_2,height_2)
    X_target.append(train_img_resize)
    i += 1

100%|████████████████████████████████████████████████████████████████████████████| 10000/10000 [04:35<00:00, 36.24it/s]


np.array(X_target).shape

(10000, 224, 224, 3)


fileName = "Images_X_3_target_10000.pkl"
fileObject = open(fileName, 'wb')

pkl.dump(X_target, fileObject)
fileObject.close()


Images_10000_Target.to_pickle("Images_10000_Target.pkl") # pickling the proportionate target from 10000 Images dataframe


image_X_3 = open ("Images_X_3_target_10000.pkl", "rb")
Images_10000_pkl= open ("Images_10000_Target.pkl", "rb")
X = pkl.load(image_X_3)
Images_10000_Target = pkl.load(Images_10000_pkl)


X[0:2]

[array([[[255, 245, 237],
         [255, 238, 222],
         [255, 230, 205],
         ...,
         [255, 246, 239],
         [255, 249, 245],
         [255, 253, 252]],
 
        [[255, 219, 184],
         [255, 212, 171],
         [255, 200, 145],
         ...,
         [255, 221, 188],
         [255, 226, 198],
         [255, 242, 231]],
 
        [[255, 194, 134],
         [255, 188, 122],
         [255, 174,  94],
         ...,
         [255, 195, 134],
         [255, 201, 147],
         [255, 218, 182]],
 
        ...,
 
        [[  0,  40,  39],
         [  0,  40,  39],
         [  0,  40,  39],
         ...,
         [  0,  38,  37],
         [  0,  38,  37],
         [  0,  40,  39]],
 
        [[  0,  40,  39],
         [  0,  39,  38],
         [  0,  40,  39],
         ...,
         [  0,  38,  37],
         [  0,  38,  37],
         [  0,  40,  39]],
 
        [[  0,  40,  39],
         [  0,  39,  38],
         [  0,  40,  39],
         ...,
         [  0,  38,  37],
         [  0,  38,  37],
         [  0,  40,  39]]], dtype=uint8),
 array([[[  0,  48,  47],
         [  0,  47,  46],
         [  0,  44,  43],
         ...,
         [  0,  36,  35],
         [  0,  38,  37],
         [  0,  34,  33]],
 
        [[  0,  47,  45],
         [  0,  44,  43],
         [  0,  44,  43],
         ...,
         [  0,  33,  32],
         [  0,  33,  32],
         [  0,  33,  32]],
 
        [[  0,  44,  43],
         [  0,  43,  42],
         [  0,  40,  39],
         ...,
         [  0,  36,  35],
         [  0,  36,  35],
         [  0,  36,  35]],
 
        ...,
 
        [[228, 101,  53],
         [233, 106,  44],
         [239, 112,  32],
         ...,
         [  0,  43,  43],
         [  0,  46,  45],
         [  0,  39,  38]],
 
        [[233, 106,  44],
         [231, 104,  47],
         [240, 113,  30],
         ...,
         [  0,  40,  39],
         [  0,  48,  47],
         [  0,  40,  40]],
 
        [[235, 108,  40],
         [242, 115,  26],
         [245, 119,  20],
         ...,
         [  0,  44,  43],
         [  0,  50,  49],
         [  0,  38,  37]]], dtype=uint8)]


X_target = X

np.array(X_target).shape

(10000, 224, 224, 3)


X_tar_10k = np.array(X_target)


X_tar_10k.shape

(10000, 224, 224, 3)


print(X_tar_10k.max())
print('\n',X_tar_10k.min())

255

 0


y1_tar_10k = Images_10000_Target['Image_Target']


y1_tar_10k.value_counts()

0    5000
1    5000
Name: Image_Target, dtype: int64


y1_tar_cat_10k = to_categorical(y1_tar_10k, num_classes=2)

print("Shape of y1_tar_cat_10k:", y1_tar_cat_10k.shape)

Shape of y1_tar_cat_10k: (10000, 2)


y1_tar_cat_10k[0:10]

array([[1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.]], dtype=float32)


print("X_tar_10k shape",X_tar_10k.shape)
print("y1_tar_cat_10k shape",y1_tar_cat_10k.shape)

X_tar_10k shape (10000, 224, 224, 3)
y1_tar_cat_10k shape (10000, 2)


X_train, X_test, y_train, y_test = train_test_split(X_tar_10k, y1_tar_cat_10k, test_size=.20, 
                                                    stratify=y1_tar_cat_10k, random_state=1)  # 80% Training and 20% Testing


print("Shape of X_train",X_train.shape)
print("\nShape of y_train",y_train.shape)  

print("\nShape of X_test",X_test.shape)
print("\nShape of y_test",y_test.shape)

Shape of X_train (8000, 224, 224, 3)

Shape of y_train (8000, 2)

Shape of X_test (2000, 224, 224, 3)

Shape of y_test (2000, 2)


X_train1, X_val, y_train1, y_val = train_test_split(X_train, y_train, test_size=.20, stratify=y_train, random_state=1)  # 80% Training and 20% Testing


print("Shape of X_train1",X_train1.shape)
print("\nShape of y_train1",y_train1.shape)  

print("\nShape of X_val",X_val.shape)
print("\nShape of y_val",y_val.shape)

Shape of X_train1 (6400, 224, 224, 3)

Shape of y_train1 (6400, 2)

Shape of X_val (1600, 224, 224, 3)

Shape of y_val (1600, 2)


print(X_val[0])
print(y_val[0])

[[[145  20 217]
  [ 75  53 201]
  [  8 119 133]
  ...
  [  0   0   0]
  [  0   0   0]
  [  0   0   0]]

 [[212  85  86]
  [129  10 238]
  [ 47  80 173]
  ...
  [  0   0   0]
  [  0   0   0]
  [  0   0   0]]

 [[237 110  35]
  [158  30 196]
  [106  20 233]
  ...
  [  0   0   0]
  [  0   0   0]
  [  0   0   0]]

 ...

 [[171  43 170]
  [133   6 244]
  [ 90  37 217]
  ...
  [  0  78  76]
  [  0  81  79]
  [  0  79  77]]

 [[255 154  51]
  [250 127  20]
  [226  99  58]
  ...
  [ 37  90 163]
  [ 38  88 164]
  [ 41  85 167]]

 [[255 219 184]
  [255 208 162]
  [255 207 160]
  ...
  [170  42 172]
  [172  44 167]
  [174  46 163]]]
[1. 0.]


tf.keras.backend.clear_session()


batch_size = 64
nb_epochs = 10
history = model.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
100/100 [==============================] - 53s 525ms/step - loss: 0.5658 - accuracy: 0.7228 - val_loss: 0.8308 - val_accuracy: 0.6550
Epoch 2/10
100/100 [==============================] - 53s 530ms/step - loss: 0.5360 - accuracy: 0.7403 - val_loss: 0.5387 - val_accuracy: 0.7281
Epoch 3/10
100/100 [==============================] - 53s 531ms/step - loss: 0.5084 - accuracy: 0.7550 - val_loss: 0.5603 - val_accuracy: 0.7206
Epoch 4/10
100/100 [==============================] - 53s 527ms/step - loss: 0.4901 - accuracy: 0.7652 - val_loss: 0.5544 - val_accuracy: 0.7275


plt.figure(figsize=(6,4))
plt.plot(history.history['loss'], label='train')
plt.plot(history.history['val_loss'], label='test')
plt.legend()
plt.show()


plt.figure(figsize=(6,4))
plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='test')
plt.legend()
plt.show()


tf.keras.backend.clear_session()

batch_size = 64
nb_epochs = 10
history_2 = model_2.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.0001)])

Epoch 1/10
100/100 [==============================] - 53s 533ms/step - loss: 0.5336 - accuracy: 0.7456 - val_loss: 0.5606 - val_accuracy: 0.7287
Epoch 2/10
100/100 [==============================] - 53s 533ms/step - loss: 0.4929 - accuracy: 0.7686 - val_loss: 0.5542 - val_accuracy: 0.7244
Epoch 3/10
100/100 [==============================] - 53s 532ms/step - loss: 0.4625 - accuracy: 0.7853 - val_loss: 0.5424 - val_accuracy: 0.7431
Epoch 4/10
100/100 [==============================] - 54s 541ms/step - loss: 0.4185 - accuracy: 0.8103 - val_loss: 0.5951 - val_accuracy: 0.7069
Epoch 5/10
100/100 [==============================] - 54s 536ms/step - loss: 0.3710 - accuracy: 0.8384 - val_loss: 0.5894 - val_accuracy: 0.7431
Epoch 6/10
100/100 [==============================] - 54s 542ms/step - loss: 0.3137 - accuracy: 0.8622 - val_loss: 0.7019 - val_accuracy: 0.6675


plt.figure(figsize=(6,4))
plt.plot(history_2.history['loss'], label='train')
plt.plot(history_2.history['val_loss'], label='test')
plt.legend()
plt.show()


plt.figure(figsize=(6,4))
plt.plot(history_2.history['accuracy'], label='train')
plt.plot(history_2.history['val_accuracy'], label='test')
plt.legend()
plt.show()


tf.keras.backend.clear_session()

batch_size = 64
nb_epochs = 10
history_2a = model_2a.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.01)])

Epoch 1/10
100/100 [==============================] - 281s 3s/step - loss: 0.5493 - accuracy: 0.7348 - val_loss: 0.5557 - val_accuracy: 0.7294
Epoch 2/10
100/100 [==============================] - 326s 3s/step - loss: 0.4865 - accuracy: 0.7680 - val_loss: 0.5466 - val_accuracy: 0.7613
Epoch 3/10
100/100 [==============================] - 323s 3s/step - loss: 0.4405 - accuracy: 0.7980 - val_loss: 0.5561 - val_accuracy: 0.7287
Epoch 4/10
100/100 [==============================] - 325s 3s/step - loss: 0.3928 - accuracy: 0.8253 - val_loss: 0.5607 - val_accuracy: 0.7450


plt.figure(figsize=(6,4))
plt.plot(history_2a.history['loss'], label='train')
plt.plot(history_2a.history['val_loss'], label='test')
plt.legend()
plt.show()


plt.figure(figsize=(6,4))
plt.plot(history_2a.history['accuracy'], label='train')
plt.plot(history_2a.history['val_accuracy'], label='test')
plt.legend()
plt.show()


tf.keras.backend.clear_session()

batch_size = 64
nb_epochs = 10
history_3 = model_3.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.001)])

Epoch 1/10
100/100 [==============================] - 301s 3s/step - loss: 0.5468 - accuracy: 0.7325 - val_loss: 0.5153 - val_accuracy: 0.7525
Epoch 2/10
100/100 [==============================] - 299s 3s/step - loss: 0.5152 - accuracy: 0.7533 - val_loss: 0.5256 - val_accuracy: 0.7362
Epoch 3/10
100/100 [==============================] - 298s 3s/step - loss: 0.5022 - accuracy: 0.7570 - val_loss: 0.5226 - val_accuracy: 0.7350
Epoch 4/10
100/100 [==============================] - 297s 3s/step - loss: 0.4877 - accuracy: 0.7659 - val_loss: 0.5242 - val_accuracy: 0.7381


plt.figure(figsize=(6,4))
plt.plot(history_3.history['loss'], label='train')
plt.plot(history_3.history['val_loss'], label='test')
plt.legend()
plt.show()


plt.figure(figsize=(6,4))
plt.plot(history_3.history['accuracy'], label='train')
plt.plot(history_3.history['val_accuracy'], label='test')
plt.legend()
plt.show()


loss_1, accuracy_1 = model.evaluate(X_test,y_test)

63/63 [==============================] - 5s 79ms/step - loss: 0.5370 - accuracy: 0.7410


y_pred_1 = model.predict(X_test)

63/63 [==============================] - 5s 77ms/step


y_test_arg=np.argmax(y_test,axis=1)
Y_pred_1 = np.argmax(y_pred_1,axis=1)


RESULTS = pd.DataFrame()

RESULTS = RESULTS.append(pd.Series(["model",np.round((loss_1*100),2),np.round((accuracy_1*100),2)]),ignore_index=True)


cm = confusion_matrix(y_test_arg, Y_pred_1)

labels = ["0 - No Pneumonia", "1 - Pneumonia"]

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)

disp.plot()

plt.show()


loss_2, accuracy_2 = model_2.evaluate(X_test,y_test)

63/63 [==============================] - 5s 83ms/step - loss: 0.7691 - accuracy: 0.6565


y_pred_2 = model_2.predict(X_test)

63/63 [==============================] - 5s 82ms/step


Y_pred_2 = np.argmax(y_pred_2,axis=1)

RESULTS = RESULTS.append(pd.Series(["model_2",np.round((loss_2*100),2),np.round((accuracy_2*100),2)]),ignore_index=True)

cm = confusion_matrix(y_test_arg, Y_pred_2)

labels = ["0 - No Pneumonia", "1 - Pneumonia"]

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)

disp.plot()

plt.show()


print(classification_report(y_test_arg, Y_pred_2))

              precision    recall  f1-score   support

           0       0.62      0.82      0.70      1000
           1       0.73      0.49      0.59      1000

    accuracy                           0.66      2000
   macro avg       0.68      0.66      0.65      2000
weighted avg       0.68      0.66      0.65      2000


loss_2a, accuracy_2a = model_2a.evaluate(X_test,y_test)

63/63 [==============================] - 19s 304ms/step - loss: 0.5580 - accuracy: 0.7450


y_pred_2a = model.predict(X_test)

63/63 [==============================] - 5s 76ms/step


Y_pred_2a = np.argmax(y_pred_2a,axis=1)

RESULTS = RESULTS.append(pd.Series(["model_2a",np.round((loss_2a*100),2),np.round((accuracy_2a*100),2)]),ignore_index=True)

cm = confusion_matrix(y_test_arg, Y_pred_2a)

labels = ["0 - No Pneumonia", "1 - Pneumonia"]

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)

disp.plot()

plt.show()


print(classification_report(y_test_arg, Y_pred_2a))

              precision    recall  f1-score   support

           0       0.71      0.81      0.76      1000
           1       0.78      0.67      0.72      1000

    accuracy                           0.74      2000
   macro avg       0.75      0.74      0.74      2000
weighted avg       0.75      0.74      0.74      2000


loss_3, accuracy_3 = model_3.evaluate(X_test,y_test)

63/63 [==============================] - 18s 277ms/step - loss: 0.5284 - accuracy: 0.7295


y_pred_3 = model.predict(X_test)

63/63 [==============================] - 5s 77ms/step


Y_pred_3 = np.argmax(y_pred_3,axis=1)

RESULTS = RESULTS.append(pd.Series(["model_3",np.round((loss_3*100),2),np.round((accuracy_3*100),2)]),ignore_index=True)

cm = confusion_matrix(y_test_arg, Y_pred_3)

labels = ["0 - No Pneumonia", "1 - Pneumonia"]

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)

disp.plot()

plt.show()


print(classification_report(y_test_arg, Y_pred_3))

              precision    recall  f1-score   support

           0       0.71      0.81      0.76      1000
           1       0.78      0.67      0.72      1000

    accuracy                           0.74      2000
   macro avg       0.75      0.74      0.74      2000
weighted avg       0.75      0.74      0.74      2000


RESULTS.columns = ['MODEL_NAME','LOSS','ACCURACY']

RESULTS.style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()


RESULTS.to_csv("FINE_TUNING_10000_IMAGES.csv",index = False)


# Convert the dicom images to jpeg format.

dst = "Training_jpg"

if os.path.exists(dst):
    print("Path already exists")
else:
    os.mkdir(dst)
    
jpeg_path = os.path.join(dst+"/")

folder_path = "stage_2_train_images"
dicom_path = os.listdir("stage_2_train_images")

NAMES_DCM = []
NAMES_JPG = []

for n, image in enumerate(dicom_path):
    ds = dicom.dcmread(os.path.join(folder_path, image))
    NAMES_DCM.append(image)
    new_image = ds.pixel_array.astype(float)
    scaled_image = (np.maximum(new_image, 0) / new_image.max()) * 255.0
    scaled_image = np.uint8(scaled_image)
    final_image = Image.fromarray(scaled_image)
    final_image.save(jpeg_path + image.replace('.dcm','.jpg'))
    NAMES_JPG.append(image.replace('.dcm','.jpg'))

Path already exists


# Convert the dicom images to jpeg format.

dst = "Training_jpg"

if os.path.exists(dst):
    print("Path already exists")
else:
    os.mkdir(dst)
    
jpeg_path = os.path.join(dst+"/")

folder_path = "stage_2_train_images"
dicom_path = os.listdir("stage_2_train_images")

NAMES_DCM = []
NAMES_JPG = []

for image in tqdm(Images_6000_Target["Image_file"].values):
    ds = dicom.dcmread(os.path.join(folder_path, image))
    NAMES_DCM.append(image)
    new_image = ds.pixel_array.astype(float)
    scaled_image = (np.maximum(new_image, 0) / new_image.max()) * 255.0
    scaled_image = np.uint8(scaled_image)
    final_image = Image.fromarray(scaled_image)
    final_image.save(jpeg_path + image.replace('.dcm','.jpg'))
    NAMES_JPG.append(image.replace('.dcm','.jpg'))

Path already exists

100%|██████████████████████████████████████████████████████████████████████████████| 6000/6000 [03:20<00:00, 30.00it/s]


NAMES_JPG = pd.Series(NAMES_JPG)
NAMES_DCM = pd.Series(NAMES_DCM)

NAMES_DF = {"NAMES_JPG" : NAMES_JPG, "NAMES_DCM" : NAMES_DCM}

NAMES_DF = pd.DataFrame(NAMES_DF)


NAMES_DF_FINAL = pd.merge(NAMES_DF, Images_df, left_on='NAMES_DCM', right_on='Image_file')


NAMES_DF_FINAL = NAMES_DF_FINAL.drop(columns = ["Folder","Image_file","Full_filename"])

NAMES_DF_FINAL["Full_filename"] = "Training_jpg"+"/"+NAMES_DF_FINAL["NAMES_JPG"]

NAMES_DF_FINAL.sample(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()


NAMES_DF_FINAL.to_pickle("NAMES_DF_FINAL.pkl") # pickling the jpg image data


load_data = open("NAMES_DF_FINAL.pkl", "rb")

NAMES_DF_FINAL = pickle.load(load_data)


def show_jpg_images(passed_value):
    show_df = NAMES_DF_FINAL.sample(n=passed_value)

    if passed_value/5 == 0:
        z = math.ceil(passed_value/5)
    else:
            z = math.ceil((passed_value)/5)

    fig, ax = plt.subplots(z,5,constrained_layout=True)
    fig.set_figheight(20)
    fig.set_figwidth(20)
    
    i = 0
    j = 0

    for index, row in show_df.iterrows():
        image_path = row['Full_filename']
        jpg_image = cv2.imread(image_path)
        jpg_image = cv2.resize(jpg_image, dsize=(224,224))

        height_1 = 224
        width_1 = 224
        dim = (width_1, height_1)

        ax[i][j].imshow(jpg_image)
        ax[i][j].set_title(row['Image_Class'])
        
        j = j+1
        if j == 5:
            i = i+1
            j = 0
    plt.figure().clear()
    plt.show()
    return(show_df)


show_jpg_images(20)

<Figure size 640x480 with 0 Axes>


sample_1_jpg = NAMES_DF_FINAL[NAMES_DF_FINAL["Image_Target"] == 0].sample(n = 3000)

sample_2_jpg = NAMES_DF_FINAL[NAMES_DF_FINAL["Image_Target"] == 1].sample(n = 3000)


samples_jpg = [sample_1_jpg,sample_2_jpg]


result_jpg = pd.concat(samples_jpg)


print("Shape of the data selected for model building: ", result_jpg.shape)

print("\nNo.of rows of data selected for target 0 and 1:\n",result_jpg["Image_Target"].value_counts())

Shape of the data selected for model building:  (6000, 6)

No.of rows of data selected for target 0 and 1:
 0    3000
1    3000
Name: Image_Target, dtype: int64


images_6000_jpg = result_jpg.copy()


height = 224
width = 224
dim = (height, width)

X_target = []
brk = 0
i = 1 # initialisation

for img in tqdm(images_6000_jpg["Full_filename"].values):
    try:
        train_img_resize = cv2.resize(cv2.imread(img),dim, interpolation=cv2.INTER_LINEAR)
    except:
        brk+=1
        print("breaking out for image ",img)
    height_2, width_2, layers = train_img_resize.shape
    size=(width_2,height_2)
    X_target.append(train_img_resize)
    i += 1

100%|█████████████████████████████████████████████████████████████████████████████| 6000/6000 [00:43<00:00, 139.11it/s]


X_target =  np.asarray(X_target)


fileName = "Images_jpg_target_6000.pkl"
fileObject = open(fileName, 'wb')

pkl.dump(X_target, fileObject)
fileObject.close()


images_6000_jpg.to_pickle("images_6000_jpg.pkl") # pickling the proportionate target from 6000 jpg Images dataframe


image_pickle = open ("images_6000_jpg.pkl", "rb")
images_6000_jpg = pkl.load(image_pickle)
images_6000_jpg.sample(10).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


image_jpg = open ("Images_jpg_target_6000.pkl", "rb")
X = pkl.load(image_jpg)
X[0:2]

array([[[[  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0],
         ...,
         [  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0]],

        [[  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0],
         ...,
         [  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0]],

        [[  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0],
         ...,
         [  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0]],

        ...,

        [[  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0],
         ...,
         [  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0]],

        [[  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0],
         ...,
         [  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0]],

        [[  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0],
         ...,
         [  0,   0,   0],
         [  0,   0,   0],
         [  0,   0,   0]]],


       [[[218, 218, 218],
         [213, 213, 213],
         [206, 206, 206],
         ...,
         [ 16,  16,  16],
         [ 16,  16,  16],
         [ 14,  14,  14]],

        [[207, 207, 207],
         [203, 203, 203],
         [194, 194, 194],
         ...,
         [ 16,  16,  16],
         [ 16,  16,  16],
         [ 14,  14,  14]],

        [[200, 200, 200],
         [192, 192, 192],
         [183, 183, 183],
         ...,
         [ 16,  16,  16],
         [ 16,  16,  16],
         [ 14,  14,  14]],

        ...,

        [[223, 223, 223],
         [217, 217, 217],
         [200, 200, 200],
         ...,
         [ 16,  16,  16],
         [ 16,  16,  16],
         [ 14,  14,  14]],

        [[226, 226, 226],
         [221, 221, 221],
         [204, 204, 204],
         ...,
         [ 17,  17,  17],
         [ 17,  17,  17],
         [ 17,  17,  17]],

        [[229, 229, 229],
         [224, 224, 224],
         [207, 207, 207],
         ...,
         [ 19,  19,  19],
         [ 20,  20,  20],
         [ 20,  20,  20]]]], dtype=uint8)


X_tar = np.array(X)


X_tar.shape

(6000, 224, 224, 3)


print(X_tar.max())
print(X_tar.min())

255
0


y1_tar = images_6000_jpg['Image_Target']


y1_tar.value_counts()

0    3000
1    3000
Name: Image_Target, dtype: int64


y1_tar_cat = to_categorical(y1_tar, num_classes=2)

print("Shape of y1_tar_cat:", y1_tar_cat.shape)

Shape of y1_tar_cat: (6000, 2)


y1_tar_cat[0:10]

array([[1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.]], dtype=float32)


print("X_tar shape",X_tar.shape)
print("y1_tar_cat shape",y1_tar_cat.shape)

X_tar shape (6000, 224, 224, 3)
y1_tar_cat shape (6000, 2)


X_train, X_test, y_train, y_test = train_test_split(X_tar, y1_tar_cat, test_size=.20, stratify=y1_tar_cat, random_state=1)  # 80% Training and 20% Testing


print("X_train",X_train.shape)
print("y_train",y_train.shape)  

print("X_test",X_test.shape)
print("y_test",y_test.shape)

X_train (4800, 224, 224, 3)
y_train (4800, 2)
X_test (1200, 224, 224, 3)
y_test (1200, 2)


X_train1, X_val, y_train1, y_val = train_test_split(X_train, y_train, test_size=.20, stratify=y_train, random_state=1)  # 80% Training and 20% Testing


print("X_train1",X_train1.shape)
print("y_train1",y_train1.shape)  

print("X_val",X_val.shape)
print("y_val",y_val.shape)

X_train1 (3840, 224, 224, 3)
y_train1 (3840, 2)
X_val (960, 224, 224, 3)
y_val (960, 2)


print(X_val[0])
print(y_val[0])

[[[ 7  7  7]
  [16 16 16]
  [20 20 20]
  ...
  [18 18 18]
  [16 16 16]
  [16 16 16]]

 [[16 16 16]
  [28 28 28]
  [29 29 29]
  ...
  [23 23 23]
  [19 19 19]
  [19 19 19]]

 [[24 24 24]
  [37 37 37]
  [35 35 35]
  ...
  [26 26 26]
  [26 26 26]
  [23 23 23]]

 ...

 [[ 4  4  4]
  [ 5  5  5]
  [ 5  5  5]
  ...
  [ 5  5  5]
  [ 6  6  6]
  [ 6  6  6]]

 [[ 4  4  4]
  [ 4  4  4]
  [ 4  4  4]
  ...
  [ 4  4  4]
  [ 7  7  7]
  [ 7  7  7]]

 [[ 5  5  5]
  [ 4  4  4]
  [ 4  4  4]
  ...
  [ 4  4  4]
  [ 6  6  6]
  [ 6  6  6]]]
[1. 0.]


batch_size = 60
nb_epochs = 10
history = model.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
64/64 [==============================] - 44s 681ms/step - loss: 0.4550 - accuracy: 0.7818 - val_loss: 0.6614 - val_accuracy: 0.6990
Epoch 2/10
64/64 [==============================] - 47s 732ms/step - loss: 0.4115 - accuracy: 0.8094 - val_loss: 0.5971 - val_accuracy: 0.7312
Epoch 3/10
64/64 [==============================] - 46s 726ms/step - loss: 0.3861 - accuracy: 0.8227 - val_loss: 0.6779 - val_accuracy: 0.7167
Epoch 4/10
64/64 [==============================] - 46s 718ms/step - loss: 0.3687 - accuracy: 0.8310 - val_loss: 0.7029 - val_accuracy: 0.7031


batch_size = 60
nb_epochs = 10
history = model_2.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
64/64 [==============================] - 48s 748ms/step - loss: 0.6006 - accuracy: 0.6977 - val_loss: 0.6436 - val_accuracy: 0.6969
Epoch 2/10
64/64 [==============================] - 47s 731ms/step - loss: 0.5322 - accuracy: 0.7435 - val_loss: 0.5589 - val_accuracy: 0.7240
Epoch 3/10
64/64 [==============================] - 46s 718ms/step - loss: 0.4791 - accuracy: 0.7750 - val_loss: 0.5601 - val_accuracy: 0.7240
Epoch 4/10
64/64 [==============================] - 46s 724ms/step - loss: 0.4472 - accuracy: 0.7857 - val_loss: 0.5573 - val_accuracy: 0.7281
Epoch 5/10
64/64 [==============================] - 46s 721ms/step - loss: 0.4129 - accuracy: 0.8115 - val_loss: 0.6046 - val_accuracy: 0.6865
Epoch 6/10
64/64 [==============================] - 46s 726ms/step - loss: 0.3530 - accuracy: 0.8391 - val_loss: 0.6024 - val_accuracy: 0.7208


batch_size = 64
nb_epochs = 10
history = model_2a.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 196s 3s/step - loss: 0.5938 - accuracy: 0.6948 - val_loss: 0.6786 - val_accuracy: 0.5938
Epoch 2/10
60/60 [==============================] - 193s 3s/step - loss: 0.5177 - accuracy: 0.7471 - val_loss: 0.6701 - val_accuracy: 0.6115
Epoch 3/10
60/60 [==============================] - 191s 3s/step - loss: 0.4903 - accuracy: 0.7589 - val_loss: 0.6044 - val_accuracy: 0.7167
Epoch 4/10
60/60 [==============================] - 191s 3s/step - loss: 0.4593 - accuracy: 0.7802 - val_loss: 0.6682 - val_accuracy: 0.6542
Epoch 5/10
60/60 [==============================] - 190s 3s/step - loss: 0.4254 - accuracy: 0.7945 - val_loss: 0.5609 - val_accuracy: 0.7292
Epoch 6/10
60/60 [==============================] - 190s 3s/step - loss: 0.3949 - accuracy: 0.8151 - val_loss: 0.5965 - val_accuracy: 0.7500
Epoch 7/10
60/60 [==============================] - 190s 3s/step - loss: 0.3667 - accuracy: 0.8349 - val_loss: 0.6143 - val_accuracy: 0.7167


batch_size = 64
nb_epochs = 10
history = model_3.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 177s 3s/step - loss: 0.5916 - accuracy: 0.6953 - val_loss: 0.5836 - val_accuracy: 0.7240
Epoch 2/10
60/60 [==============================] - 175s 3s/step - loss: 0.5511 - accuracy: 0.7253 - val_loss: 0.7166 - val_accuracy: 0.5406
Epoch 3/10
60/60 [==============================] - 175s 3s/step - loss: 0.5314 - accuracy: 0.7404 - val_loss: 0.5640 - val_accuracy: 0.7271
Epoch 4/10
60/60 [==============================] - 175s 3s/step - loss: 0.5120 - accuracy: 0.7479 - val_loss: 0.7512 - val_accuracy: 0.5542
Epoch 5/10
60/60 [==============================] - 154s 3s/step - loss: 0.5021 - accuracy: 0.7578 - val_loss: 0.5693 - val_accuracy: 0.7115


RESULTS = pd.DataFrame()

loss_1, accuracy_1 = model.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model",np.round((loss_1*100),2),np.round((accuracy_1*100),2)]),ignore_index=True)

loss_2, accuracy_2 = model_2.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2",np.round((loss_2*100),2),np.round((accuracy_2*100),2)]),ignore_index=True)

loss_2a, accuracy_2a = model_2a.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2a",np.round((loss_2a*100),2),np.round((accuracy_2a*100),2)]),ignore_index=True)

loss_3, accuracy_3 = model_3.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_3",np.round((loss_3*100),2),np.round((accuracy_3*100),2)]),ignore_index=True)

38/38 [==============================] - 2s 48ms/step - loss: 0.7589 - accuracy: 0.6900
38/38 [==============================] - 2s 47ms/step - loss: 0.5970 - accuracy: 0.7050
38/38 [==============================] - 8s 198ms/step - loss: 0.6151 - accuracy: 0.6867
38/38 [==============================] - 7s 177ms/step - loss: 0.5346 - accuracy: 0.7383


RESULTS.columns = ['MODEL_NAME','LOSS','ACCURACY']

RESULTS.style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()


RESULTS.to_csv("FINE_TUNING_TO_JPG_RESULTS.csv")


# Convert JPEG images to 128*128

height = 128
width = 128
dim = (height, width)

X_target = []
brk = 0
i = 1 # initialisation

for img in tqdm(images_6000_jpg["Full_filename"].values):
    try:
        train_img_resize = cv2.resize(cv2.imread(img),dim, interpolation=cv2.INTER_LINEAR)
    except:
        brk+=1
        print("breaking out for image ",img)
    height_2, width_2, layers = train_img_resize.shape
    size=(width_2,height_2)
    X_target.append(train_img_resize)
    i += 1

100%|█████████████████████████████████████████████████████████████████████████████| 6000/6000 [00:38<00:00, 155.36it/s]


X_tar =  np.asarray(X_target)

y1_tar = images_6000_jpg['Image_Target']

y1_tar.value_counts()

0    3000
1    3000
Name: Image_Target, dtype: int64


y1_tar_cat = to_categorical(y1_tar, num_classes=2)

print("Shape of y1_tar_cat:", y1_tar_cat.shape)

Shape of y1_tar_cat: (6000, 2)


y1_tar_cat[0:10]

array([[1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.]], dtype=float32)


print("X_tar shape",X_tar.shape)
print("y1_tar_cat shape",y1_tar_cat.shape)

X_tar shape (6000, 128, 128, 3)
y1_tar_cat shape (6000, 2)


X_train, X_test, y_train, y_test = train_test_split(X_tar, y1_tar_cat, test_size=.20, stratify=y1_tar_cat, random_state=1)  # 80% Training and 20% Testing

print("X_train",X_train.shape)
print("y_train",y_train.shape)

X_train (4800, 128, 128, 3)
y_train (4800, 2)


print("X_test",X_test.shape)
print("y_test",y_test.shape)

X_test (1200, 128, 128, 3)
y_test (1200, 2)


X_train1, X_val, y_train1, y_val = train_test_split(X_train, y_train, test_size=.20, stratify=y_train, random_state=1)  # 80% Training and 20% Testing

print("X_train1",X_train1.shape)
print("y_train1",y_train1.shape)  

print("X_val",X_val.shape)
print("y_val",y_val.shape)

X_train1 (3840, 128, 128, 3)
y_train1 (3840, 2)
X_val (960, 128, 128, 3)
y_val (960, 2)


print(X_val[0])
print(y_val[0])

[[[18 18 18]
  [25 25 25]
  [28 28 28]
  ...
  [22 22 22]
  [19 19 19]
  [15 15 15]]

 [[34 34 34]
  [36 36 36]
  [41 41 41]
  ...
  [30 30 30]
  [27 27 27]
  [24 24 24]]

 [[42 42 42]
  [45 45 45]
  [46 46 46]
  ...
  [41 41 41]
  [37 37 37]
  [33 33 33]]

 ...

 [[ 5  5  5]
  [ 5  5  5]
  [ 5  5  5]
  ...
  [ 5  5  5]
  [ 5  5  5]
  [ 6  6  6]]

 [[ 5  5  5]
  [ 5  5  5]
  [ 5  5  5]
  ...
  [ 5  5  5]
  [ 5  5  5]
  [ 6  6  6]]

 [[ 6  6  6]
  [ 6  6  6]
  [ 5  5  5]
  ...
  [ 6  6  6]
  [ 6  6  6]
  [ 5  5  5]]]
[1. 0.]


tf.keras.backend.clear_session()

# Initialize the model
model = Sequential()

# Add a Convolutional Layer with 32 filters of size 7X7, strides(2,2) and activation function as 'relu' 
model.add(Conv2D(filters=32, kernel_size=7, strides=(2, 2), activation="relu", input_shape=(128, 128, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())

# Add a Convolutional Layer with 64 filters of size 5X5, strides(2,2) and activation function as 'relu' 
model.add(Conv2D(filters=64, kernel_size=5, strides=(1, 1), activation="relu"))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))

# model.add(Dropout(rate=0.3))

# model.add(GlobalMaxPooling2D())

# Apply Dropout with 0.2 probability 
# model.add(Dropout(rate=0.2))

# Add a Convolutional Layer with 64 filters of size 3X3 and activation function as 'relu' 
# model.add(Conv2D(filters=64, kernel_size=3, strides=(2, 2), activation="relu"))
# model.add(MaxPooling2D(pool_size=(2, 2)))
# model.add(BatchNormalization())

# Flatten the layer
model.add(Flatten())

# Add Fully Connected Layer with 128 units and activation function as 'relu'
model.add(Dense(128, activation="relu"))
model.add(Dense(64, activation="relu"))

# model.add(Dropout(rate=0.3))

# model.add(BatchNormalization())

# model.add(Dense(128, activation="relu"))

#Add Fully Connected Layer with 3 units and activation function as 'softmax'
model.add(Dense(2, activation="sigmoid"))


optimizer = Adam(learning_rate=0.01)
model.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 109, 109, 32)      4736      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 54, 54, 32)       0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 54, 54, 32)       128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 50, 50, 64)        51264     
                                                                 
 batch_normalization_1 (Batc  (None, 50, 50, 64)       256       
 hNormalization)                                                 
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 25, 25, 64)       0         
 2D)                                                             
                                                                 
 flatten (Flatten)           (None, 40000)             0         
                                                                 
 dense (Dense)               (None, 128)               5120128   
                                                                 
 dense_1 (Dense)             (None, 64)                8256      
                                                                 
 dense_2 (Dense)             (None, 2)                 130       
                                                                 
=================================================================
Total params: 5,184,898
Trainable params: 5,184,706
Non-trainable params: 192
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history = model.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 11s 171ms/step - loss: 2.0457 - accuracy: 0.6555 - val_loss: 10.9216 - val_accuracy: 0.6021
Epoch 2/10
60/60 [==============================] - 10s 161ms/step - loss: 0.5977 - accuracy: 0.7036 - val_loss: 1.5250 - val_accuracy: 0.6250
Epoch 3/10
60/60 [==============================] - 10s 160ms/step - loss: 0.5896 - accuracy: 0.6883 - val_loss: 0.7360 - val_accuracy: 0.6990
Epoch 4/10
60/60 [==============================] - 10s 160ms/step - loss: 0.5509 - accuracy: 0.7201 - val_loss: 0.6632 - val_accuracy: 0.7021
Epoch 5/10
60/60 [==============================] - 10s 165ms/step - loss: 0.5440 - accuracy: 0.7320 - val_loss: 0.5619 - val_accuracy: 0.7312
Epoch 6/10
60/60 [==============================] - 10s 175ms/step - loss: 0.5294 - accuracy: 0.7352 - val_loss: 0.5498 - val_accuracy: 0.7312
Epoch 7/10
60/60 [==============================] - 10s 169ms/step - loss: 0.5253 - accuracy: 0.7411 - val_loss: 0.5506 - val_accuracy: 0.7302
Epoch 8/10
60/60 [==============================] - 10s 168ms/step - loss: 0.5251 - accuracy: 0.7331 - val_loss: 0.5508 - val_accuracy: 0.7510


tf.keras.backend.clear_session()

# second model
model_2 = Sequential()

model_2.add(Conv2D(filters=32, kernel_size=7, strides=(2, 2), activation="relu", input_shape=(128, 128, 3)))
model_2.add(MaxPooling2D(pool_size=(2, 2)))
model_2.add(BatchNormalization())

model_2.add(Conv2D(filters=64, kernel_size=5, strides=(1, 1), activation="relu"))
model_2.add(MaxPooling2D(pool_size=(2, 2)))
model_2.add(BatchNormalization())

#model_2.add(Dropout(rate=0.3))

model_2.add(Conv2D(filters=128, kernel_size=3, strides=(1, 1), activation="relu"))
model_2.add(MaxPooling2D(pool_size=(2, 2)))
model_2.add(BatchNormalization())

model_2.add(Flatten())

model_2.add(Dense(128, activation="relu"))
# model_2.add(BatchNormalization())

model_2.add(Dense(84, activation="relu"))
# model_2.add(BatchNormalization())
model_2.add(Dense(42, activation="relu"))

model_2.add(Dense(2, activation="sigmoid"))


optimizer = Adam(learning_rate=0.001)
model_2.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model_2.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 61, 61, 32)        4736      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 30, 30, 32)       0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 30, 30, 32)       128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 26, 26, 64)        51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 13, 13, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 13, 13, 64)       256       
 hNormalization)                                                 
                                                                 
 conv2d_2 (Conv2D)           (None, 11, 11, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 5, 5, 128)        0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 5, 5, 128)        512       
 hNormalization)                                                 
                                                                 
 flatten (Flatten)           (None, 3200)              0         
                                                                 
 dense (Dense)               (None, 128)               409728    
                                                                 
 dense_1 (Dense)             (None, 84)                10836     
                                                                 
 dense_2 (Dense)             (None, 42)                3570      
                                                                 
 dense_3 (Dense)             (None, 2)                 86        
                                                                 
=================================================================
Total params: 554,972
Trainable params: 554,524
Non-trainable params: 448
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history_2 = model_2.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.0001)])

Epoch 1/10
60/60 [==============================] - 11s 174ms/step - loss: 0.6044 - accuracy: 0.6930 - val_loss: 1.5019 - val_accuracy: 0.5000
Epoch 2/10
60/60 [==============================] - 10s 168ms/step - loss: 0.5583 - accuracy: 0.7234 - val_loss: 0.8792 - val_accuracy: 0.5875
Epoch 3/10
60/60 [==============================] - 10s 170ms/step - loss: 0.5395 - accuracy: 0.7365 - val_loss: 0.6353 - val_accuracy: 0.7063
Epoch 4/10
60/60 [==============================] - 10s 171ms/step - loss: 0.5238 - accuracy: 0.7497 - val_loss: 0.9062 - val_accuracy: 0.5521
Epoch 5/10
60/60 [==============================] - 10s 171ms/step - loss: 0.5115 - accuracy: 0.7529 - val_loss: 0.5849 - val_accuracy: 0.6875
Epoch 6/10
60/60 [==============================] - 10s 171ms/step - loss: 0.4860 - accuracy: 0.7628 - val_loss: 0.5983 - val_accuracy: 0.6969
Epoch 7/10
60/60 [==============================] - 10s 172ms/step - loss: 0.4744 - accuracy: 0.7716 - val_loss: 0.6039 - val_accuracy: 0.7083
Epoch 8/10
60/60 [==============================] - 10s 175ms/step - loss: 0.4432 - accuracy: 0.7878 - val_loss: 0.6395 - val_accuracy: 0.7125


tf.keras.backend.clear_session()

# second model
model_2a = Sequential()

model_2a.add(Conv2D(filters=32, kernel_size=5, strides=(1, 1), activation="relu", input_shape=(128, 128, 3)))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Conv2D(filters=64, kernel_size=5, strides=(1, 1), activation="relu"))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Conv2D(filters=128, kernel_size=3, strides=(1, 1), activation="relu"))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Dropout(rate=0.2))

model_2a.add(Conv2D(filters=256, kernel_size=3, strides=(1, 1), activation="relu"))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Dropout(rate=0.2))

#model_2a.add(Conv2D(filters=512, kernel_size=3, strides=(1, 1), activation="relu"))
#model_2a.add(MaxPooling2D(pool_size=(2, 2)))
#model_2a.add(BatchNormalization())

model_2a.add(Flatten())

#model_2a.add(Dense(512, activation="relu"))
# model_2a.add(Dropout(rate=0.7))

model_2a.add(Dense(256, activation="relu"))
model_2a.add(Dropout(rate=0.25))

model_2a.add(Dense(128, activation="relu"))
# model_2a.add(Dropout(rate=0.3))

model_2a.add(Dense(64, activation="relu"))

model_2a.add(Dense(32, activation="relu"))

model_2a.add(Dense(2, activation="sigmoid"))


optimizer = Adam(learning_rate=0.0001)
model_2a.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"]) 
model_2a.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 124, 124, 32)      2432      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 62, 62, 32)       0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 62, 62, 32)       128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 58, 58, 64)        51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 29, 29, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 29, 29, 64)       256       
 hNormalization)                                                 
                                                                 
 conv2d_2 (Conv2D)           (None, 27, 27, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 13, 13, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 13, 13, 128)      512       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 13, 13, 128)       0         
                                                                 
 conv2d_3 (Conv2D)           (None, 11, 11, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 5, 5, 256)        0         
 2D)                                                             
                                                                 
 batch_normalization_3 (Batc  (None, 5, 5, 256)        1024      
 hNormalization)                                                 
                                                                 
 dropout_1 (Dropout)         (None, 5, 5, 256)         0         
                                                                 
 flatten (Flatten)           (None, 6400)              0         
                                                                 
 dense (Dense)               (None, 256)               1638656   
                                                                 
 dropout_2 (Dropout)         (None, 256)               0         
                                                                 
 dense_1 (Dense)             (None, 128)               32896     
                                                                 
 dense_2 (Dense)             (None, 64)                8256      
                                                                 
 dense_3 (Dense)             (None, 32)                2080      
                                                                 
 dense_4 (Dense)             (None, 2)                 66        
                                                                 
=================================================================
Total params: 2,106,594
Trainable params: 2,105,634
Non-trainable params: 960
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history_2a = model_2a.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.01)])

Epoch 1/10
60/60 [==============================] - 43s 704ms/step - loss: 0.6161 - accuracy: 0.6706 - val_loss: 0.6527 - val_accuracy: 0.6875
Epoch 2/10
60/60 [==============================] - 45s 748ms/step - loss: 0.5679 - accuracy: 0.7182 - val_loss: 0.6016 - val_accuracy: 0.7125
Epoch 3/10
60/60 [==============================] - 47s 783ms/step - loss: 0.5503 - accuracy: 0.7214 - val_loss: 0.5577 - val_accuracy: 0.7385
Epoch 4/10
60/60 [==============================] - 47s 789ms/step - loss: 0.5364 - accuracy: 0.7318 - val_loss: 0.5452 - val_accuracy: 0.7375
Epoch 5/10
60/60 [==============================] - 48s 792ms/step - loss: 0.5254 - accuracy: 0.7383 - val_loss: 0.5456 - val_accuracy: 0.7406
Epoch 6/10
60/60 [==============================] - 48s 801ms/step - loss: 0.5046 - accuracy: 0.7526 - val_loss: 0.5574 - val_accuracy: 0.7260
Epoch 7/10
60/60 [==============================] - 48s 800ms/step - loss: 0.5071 - accuracy: 0.7508 - val_loss: 0.5760 - val_accuracy: 0.7229


tf.keras.backend.clear_session()

# second model
model_3 = Sequential()

model_3.add(Conv2D(filters=32, kernel_size=5, strides=(1, 1), activation="relu", input_shape=(128, 128, 3)))
model_3.add(MaxPooling2D(pool_size=(2, 2)))

model_3.add(Conv2D(filters=64, kernel_size=5, strides=(1, 1), kernel_initializer='normal', activation="relu"))
model_3.add(MaxPooling2D(pool_size=(2, 2)))

model_3.add(Conv2D(filters=128, kernel_size=3, strides=(1, 1), kernel_initializer='normal', activation="relu"))
model_3.add(MaxPooling2D(pool_size=(2, 2)))

model_3.add(BatchNormalization())

model_3.add(Conv2D(filters=256, kernel_size=3, strides=(1, 1), kernel_initializer='normal', activation="relu"))
model_3.add(MaxPooling2D(pool_size=(2, 2)))

model_3.add(Dropout(rate=0.3))

model_3.add(Flatten())

model_3.add(Dense(128, kernel_initializer='normal', activation="relu"))

model_3.add(Dense(84, kernel_initializer='normal', activation="relu"))

model_3.add(Dense(42, kernel_initializer='normal', activation="relu"))

model_3.add(Dense(2, activation="sigmoid"))


optimizer = Adam(learning_rate=0.0001)
model_3.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model_3.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 124, 124, 32)      2432      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 62, 62, 32)       0         
 )                                                               
                                                                 
 conv2d_1 (Conv2D)           (None, 58, 58, 64)        51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 29, 29, 64)       0         
 2D)                                                             
                                                                 
 conv2d_2 (Conv2D)           (None, 27, 27, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 13, 13, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization (BatchN  (None, 13, 13, 128)      512       
 ormalization)                                                   
                                                                 
 conv2d_3 (Conv2D)           (None, 11, 11, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 5, 5, 256)        0         
 2D)                                                             
                                                                 
 dropout (Dropout)           (None, 5, 5, 256)         0         
                                                                 
 flatten (Flatten)           (None, 6400)              0         
                                                                 
 dense (Dense)               (None, 128)               819328    
                                                                 
 dense_1 (Dense)             (None, 84)                10836     
                                                                 
 dense_2 (Dense)             (None, 42)                3570      
                                                                 
 dense_3 (Dense)             (None, 2)                 86        
                                                                 
=================================================================
Total params: 1,257,052
Trainable params: 1,256,796
Non-trainable params: 256
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history_3 = model_3.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 38s 621ms/step - loss: 0.6269 - accuracy: 0.6630 - val_loss: 0.6533 - val_accuracy: 0.6510
Epoch 2/10
60/60 [==============================] - 42s 703ms/step - loss: 0.5656 - accuracy: 0.7167 - val_loss: 0.6310 - val_accuracy: 0.6406
Epoch 3/10
60/60 [==============================] - 43s 716ms/step - loss: 0.5524 - accuracy: 0.7279 - val_loss: 0.5767 - val_accuracy: 0.7219
Epoch 4/10
60/60 [==============================] - 43s 715ms/step - loss: 0.5385 - accuracy: 0.7365 - val_loss: 0.5473 - val_accuracy: 0.7354
Epoch 5/10
60/60 [==============================] - 43s 711ms/step - loss: 0.5307 - accuracy: 0.7388 - val_loss: 0.5597 - val_accuracy: 0.7198
Epoch 6/10
60/60 [==============================] - 43s 715ms/step - loss: 0.5076 - accuracy: 0.7560 - val_loss: 0.5916 - val_accuracy: 0.7021
Epoch 7/10
60/60 [==============================] - 43s 712ms/step - loss: 0.5026 - accuracy: 0.7578 - val_loss: 0.5732 - val_accuracy: 0.7208


RESULTS = pd.DataFrame()

loss_1, accuracy_1 = model.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model",np.round((loss_1*100),2),np.round((accuracy_1*100),2)]),ignore_index=True)

loss_2, accuracy_2 = model_2.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2",np.round((loss_2*100),2),np.round((accuracy_2*100),2)]),ignore_index=True)

loss_2a, accuracy_2a = model_2a.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2a",np.round((loss_2a*100),2),np.round((accuracy_2a*100),2)]),ignore_index=True)

loss_3, accuracy_3 = model_3.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_3",np.round((loss_3*100),2),np.round((accuracy_3*100),2)]),ignore_index=True)

38/38 [==============================] - 1s 16ms/step - loss: 0.5358 - accuracy: 0.7325
38/38 [==============================] - 1s 18ms/step - loss: 0.6166 - accuracy: 0.7183
38/38 [==============================] - 3s 62ms/step - loss: 0.5806 - accuracy: 0.7058
38/38 [==============================] - 2s 55ms/step - loss: 0.5660 - accuracy: 0.7225


RESULTS.columns = ['MODEL_NAME','LOSS','ACCURACY']

RESULTS.style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()


RESULTS.to_csv("JPG_128_128_LOSS_ACCURACY.csv",index = False)


img_rows=128
img_cols=128
dim = (img_rows, img_cols)

X_target = []
brk = 0
i = 1 # initialisation
for img in tqdm(Images_6000_Target["Full_filename"].values): 
    ds_3 = dicom.dcmread(img)
    img_3 = ds_3.pixel_array
    rgb = apply_color_lut(img_3, palette='PET')
    train_img = rgb
    try:
        train_img_resize = cv2.resize(train_img, dim, interpolation=cv2.INTER_LINEAR)
    except:
        brk +=1
        print("breaking out for",img_3)
        break
    height_2, width_2, layers = train_img_resize.shape
    size=(width_2,height_2)
    X_target.append(train_img_resize)
    i += 1

100%|██████████████████████████████████████████████████████████████████████████████| 6000/6000 [02:36<00:00, 38.33it/s]


X_tar =  np.asarray(X_target)

y1_tar = Images_6000_Target['Image_Target']

y1_tar.value_counts()

0    3000
1    3000
Name: Image_Target, dtype: int64


y1_tar_cat = to_categorical(y1_tar, num_classes=2)

print("Shape of y1_tar_cat:", y1_tar_cat.shape)

Shape of y1_tar_cat: (6000, 2)


print("X_tar shape",X_tar.shape)
print("y1_tar_cat shape",y1_tar_cat.shape)

X_tar shape (6000, 128, 128, 3)
y1_tar_cat shape (6000, 2)


X_train, X_test, y_train, y_test = train_test_split(X_tar, y1_tar_cat, test_size=.20, stratify=y1_tar_cat, random_state=1)  # 80% Training and 20% Testing

print("X_train",X_train.shape)
print("y_train",y_train.shape)  

print("X_test",X_test.shape)
print("y_test",y_test.shape)

X_train (4800, 128, 128, 3)
y_train (4800, 2)
X_test (1200, 128, 128, 3)
y_test (1200, 2)


X_train1, X_val, y_train1, y_val = train_test_split(X_train, y_train, test_size=.20, stratify=y_train, random_state=1)  # 80% Training and 20% Testing

print("X_train1",X_train1.shape)
print("y_train1",y_train1.shape)  

print("X_val",X_val.shape)
print("y_val",y_val.shape)

X_train1 (3840, 128, 128, 3)
y_train1 (3840, 2)
X_val (960, 128, 128, 3)
y_val (960, 2)


print(X_val[0])
print(y_val[0])

[[[ 0 97 95]
  [ 0 47 46]
  [ 0 19 18]
  ...
  [ 0 66 65]
  [ 0 66 64]
  [ 0 80 78]]

 [[ 0 92 90]
  [ 0 44 43]
  [ 0 16 15]
  ...
  [ 0 12 11]
  [ 0 14 13]
  [ 0 23 22]]

 [[ 0 92 90]
  [ 0 45 44]
  [ 0 14 13]
  ...
  [ 0  0  0]
  [ 0  0  0]
  [ 0  6  5]]

 ...

 [[ 0  6  5]
  [ 0  0  0]
  [ 0  0  0]
  ...
  [ 0  3  2]
  [ 0 26 25]
  [ 0 63 61]]

 [[ 0 15 14]
  [ 0  7  6]
  [ 0  7  6]
  ...
  [ 0  3  2]
  [ 0 24 23]
  [ 0 64 62]]

 [[ 0 22 21]
  [ 0 24 23]
  [ 0 17 16]
  ...
  [ 0  3  2]
  [ 0 27 26]
  [ 0 65 63]]]
[1. 0.]


batch_size = 64
nb_epochs = 10
history = model.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 30s 498ms/step - loss: 0.5331 - accuracy: 0.7383 - val_loss: 0.5408 - val_accuracy: 0.7396
Epoch 2/10
60/60 [==============================] - 29s 488ms/step - loss: 0.5188 - accuracy: 0.7427 - val_loss: 0.5625 - val_accuracy: 0.7437
Epoch 3/10
60/60 [==============================] - 29s 490ms/step - loss: 0.5063 - accuracy: 0.7417 - val_loss: 0.5367 - val_accuracy: 0.7542
Epoch 4/10
60/60 [==============================] - 29s 489ms/step - loss: 0.4823 - accuracy: 0.7615 - val_loss: 0.5322 - val_accuracy: 0.7417
Epoch 5/10
60/60 [==============================] - 29s 490ms/step - loss: 0.4720 - accuracy: 0.7638 - val_loss: 0.5391 - val_accuracy: 0.7510
Epoch 6/10
60/60 [==============================] - 29s 486ms/step - loss: 0.4721 - accuracy: 0.7651 - val_loss: 0.5544 - val_accuracy: 0.7542


batch_size = 64
nb_epochs = 10
history = model_2.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 31s 523ms/step - loss: 0.5176 - accuracy: 0.7484 - val_loss: 0.5956 - val_accuracy: 0.7125
Epoch 2/10
60/60 [==============================] - 31s 517ms/step - loss: 0.4639 - accuracy: 0.7750 - val_loss: 0.5294 - val_accuracy: 0.7552
Epoch 3/10
60/60 [==============================] - 32s 536ms/step - loss: 0.4211 - accuracy: 0.8042 - val_loss: 0.5347 - val_accuracy: 0.7573
Epoch 4/10
60/60 [==============================] - 32s 531ms/step - loss: 0.4129 - accuracy: 0.8083 - val_loss: 0.5951 - val_accuracy: 0.7063


batch_size = 64
nb_epochs = 10
history = model_2a.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 137s 2s/step - loss: 0.5868 - accuracy: 0.6919 - val_loss: 0.5295 - val_accuracy: 0.7510
Epoch 2/10
60/60 [==============================] - 141s 2s/step - loss: 0.5361 - accuracy: 0.7323 - val_loss: 0.5266 - val_accuracy: 0.7469
Epoch 3/10
60/60 [==============================] - 143s 2s/step - loss: 0.5199 - accuracy: 0.7424 - val_loss: 0.5092 - val_accuracy: 0.7552
Epoch 4/10
60/60 [==============================] - 139s 2s/step - loss: 0.5071 - accuracy: 0.7589 - val_loss: 0.5193 - val_accuracy: 0.7510
Epoch 5/10
60/60 [==============================] - 140s 2s/step - loss: 0.4805 - accuracy: 0.7661 - val_loss: 0.5187 - val_accuracy: 0.7625


batch_size = 64
nb_epochs = 10
history = model_3.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 135s 2s/step - loss: 0.5936 - accuracy: 0.6911 - val_loss: 0.6041 - val_accuracy: 0.6969
Epoch 2/10
60/60 [==============================] - 134s 2s/step - loss: 0.5390 - accuracy: 0.7310 - val_loss: 0.5610 - val_accuracy: 0.7302
Epoch 3/10
60/60 [==============================] - 133s 2s/step - loss: 0.5317 - accuracy: 0.7370 - val_loss: 0.6009 - val_accuracy: 0.6823
Epoch 4/10
60/60 [==============================] - 133s 2s/step - loss: 0.5085 - accuracy: 0.7531 - val_loss: 0.5305 - val_accuracy: 0.7490
Epoch 5/10
60/60 [==============================] - 133s 2s/step - loss: 0.4935 - accuracy: 0.7596 - val_loss: 0.5251 - val_accuracy: 0.7510
Epoch 6/10
60/60 [==============================] - 137s 2s/step - loss: 0.4869 - accuracy: 0.7664 - val_loss: 0.5264 - val_accuracy: 0.7510
Epoch 7/10
60/60 [==============================] - 146s 2s/step - loss: 0.4746 - accuracy: 0.7745 - val_loss: 0.5238 - val_accuracy: 0.7510
Epoch 8/10
60/60 [==============================] - 147s 2s/step - loss: 0.4500 - accuracy: 0.7943 - val_loss: 0.5436 - val_accuracy: 0.7354
Epoch 9/10
60/60 [==============================] - 146s 2s/step - loss: 0.4433 - accuracy: 0.7945 - val_loss: 0.5415 - val_accuracy: 0.7458


RESULTS = pd.DataFrame()

loss_1, accuracy_1 = model.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model",np.round((loss_1*100),2),np.round((accuracy_1*100),2)]),ignore_index=True)

loss_2, accuracy_2 = model_2.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2",np.round((loss_2*100),2),np.round((accuracy_2*100),2)]),ignore_index=True)

loss_2a, accuracy_2a = model_2a.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2a",np.round((loss_2a*100),2),np.round((accuracy_2a*100),2)]),ignore_index=True)

loss_3, accuracy_3 = model_3.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_3",np.round((loss_3*100),2),np.round((accuracy_3*100),2)]),ignore_index=True)

RESULTS.columns = ['MODEL_NAME','LOSS','ACCURACY']

RESULTS.style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()

38/38 [==============================] - 2s 46ms/step - loss: 0.5507 - accuracy: 0.7383
38/38 [==============================] - 2s 43ms/step - loss: 0.6108 - accuracy: 0.6792
38/38 [==============================] - 7s 189ms/step - loss: 0.5465 - accuracy: 0.7308
38/38 [==============================] - 7s 174ms/step - loss: 0.5600 - accuracy: 0.7325


RESULTS.to_csv("DICOM_128x128_Accuracy_Loss.csv",index = False)


image_X_3 = open ("Images_X_3_target_6000.pkl", "rb")
X = pkl.load(image_X_3)


X_tar = np.array(X)


y1_tar = Images_6000_Target['Image_Target']
y1_tar_cat = to_categorical(y1_tar, num_classes=2)


X_train, X_test, y_train, y_test = train_test_split(X_tar, y1_tar_cat, test_size=.20, stratify=y1_tar_cat, random_state=1)  # 80% Training and 20% Testing


X_train1, X_val, y_train1, y_val = train_test_split(X_train, y_train, test_size=.20, stratify=y_train, random_state=1)  # 80% Training and 20% validation


print("X_train",X_train.shape)
print("y_train",y_train.shape)  
print("")
print("X_test",X_test.shape)
print("y_test",y_test.shape)
print("")
print("X_val",X_val.shape)
print("y_val",y_val.shape)

X_train (4800, 224, 224, 3)
y_train (4800, 2)

X_test (1200, 224, 224, 3)
y_test (1200, 2)

X_val (960, 224, 224, 3)
y_val (960, 2)


optimizer = Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)

model.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"]) 
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 109, 109, 32)      4736      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 54, 54, 32)       0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 54, 54, 32)       128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 50, 50, 64)        51264     
                                                                 
 batch_normalization_1 (Batc  (None, 50, 50, 64)       256       
 hNormalization)                                                 
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 25, 25, 64)       0         
 2D)                                                             
                                                                 
 flatten (Flatten)           (None, 40000)             0         
                                                                 
 dense (Dense)               (None, 128)               5120128   
                                                                 
 dense_1 (Dense)             (None, 64)                8256      
                                                                 
 dense_2 (Dense)             (None, 2)                 130       
                                                                 
=================================================================
Total params: 5,184,898
Trainable params: 5,184,706
Non-trainable params: 192
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history = model.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 130s 2s/step - loss: 0.6349 - accuracy: 0.6740 - val_loss: 0.6424 - val_accuracy: 0.6448
Epoch 2/10
60/60 [==============================] - 128s 2s/step - loss: 0.4961 - accuracy: 0.7622 - val_loss: 0.5932 - val_accuracy: 0.6854
Epoch 3/10
60/60 [==============================] - 130s 2s/step - loss: 0.4248 - accuracy: 0.8089 - val_loss: 0.5791 - val_accuracy: 0.7021
Epoch 4/10
60/60 [==============================] - 129s 2s/step - loss: 0.3636 - accuracy: 0.8497 - val_loss: 0.5531 - val_accuracy: 0.7240
Epoch 5/10
60/60 [==============================] - 128s 2s/step - loss: 0.3106 - accuracy: 0.8872 - val_loss: 0.5593 - val_accuracy: 0.7156
Epoch 6/10
60/60 [==============================] - 126s 2s/step - loss: 0.2639 - accuracy: 0.9143 - val_loss: 0.5699 - val_accuracy: 0.7229


optimizer = Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)

model_2.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"]) 
model_2.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 109, 109, 32)      4736      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 54, 54, 32)       0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 54, 54, 32)       128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 50, 50, 64)        51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 25, 25, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 25, 25, 64)       256       
 hNormalization)                                                 
                                                                 
 conv2d_2 (Conv2D)           (None, 23, 23, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 11, 11, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 11, 11, 128)      512       
 hNormalization)                                                 
                                                                 
 flatten (Flatten)           (None, 15488)             0         
                                                                 
 dense (Dense)               (None, 128)               1982592   
                                                                 
 dense_1 (Dense)             (None, 84)                10836     
                                                                 
 dense_2 (Dense)             (None, 42)                3570      
                                                                 
 dense_3 (Dense)             (None, 2)                 86        
                                                                 
=================================================================
Total params: 2,127,836
Trainable params: 2,127,388
Non-trainable params: 448
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history = model_2.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 135s 2s/step - loss: 0.6248 - accuracy: 0.6591 - val_loss: 0.7425 - val_accuracy: 0.5365
Epoch 2/10
60/60 [==============================] - 134s 2s/step - loss: 0.5363 - accuracy: 0.7331 - val_loss: 0.6199 - val_accuracy: 0.6677
Epoch 3/10
60/60 [==============================] - 133s 2s/step - loss: 0.4941 - accuracy: 0.7635 - val_loss: 0.5708 - val_accuracy: 0.7115
Epoch 4/10
60/60 [==============================] - 134s 2s/step - loss: 0.4607 - accuracy: 0.7906 - val_loss: 0.5489 - val_accuracy: 0.7312
Epoch 5/10
60/60 [==============================] - 134s 2s/step - loss: 0.4323 - accuracy: 0.8130 - val_loss: 0.5407 - val_accuracy: 0.7333
Epoch 6/10
60/60 [==============================] - 135s 2s/step - loss: 0.4048 - accuracy: 0.8307 - val_loss: 0.5402 - val_accuracy: 0.7354
Epoch 7/10
60/60 [==============================] - 134s 2s/step - loss: 0.3792 - accuracy: 0.8474 - val_loss: 0.5410 - val_accuracy: 0.7396


optimizer = Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)

model_2a.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"]) 
model_2a.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 220, 220, 32)      2432      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 110, 110, 32)     0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 110, 110, 32)     128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 106, 106, 64)      51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 53, 53, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 53, 53, 64)       256       
 hNormalization)                                                 
                                                                 
 conv2d_2 (Conv2D)           (None, 51, 51, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 25, 25, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 25, 25, 128)      512       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 25, 25, 128)       0         
                                                                 
 conv2d_3 (Conv2D)           (None, 23, 23, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 11, 11, 256)      0         
 2D)                                                             
                                                                 
 batch_normalization_3 (Batc  (None, 11, 11, 256)      1024      
 hNormalization)                                                 
                                                                 
 dropout_1 (Dropout)         (None, 11, 11, 256)       0         
                                                                 
 flatten (Flatten)           (None, 30976)             0         
                                                                 
 dense (Dense)               (None, 256)               7930112   
                                                                 
 dropout_2 (Dropout)         (None, 256)               0         
                                                                 
 dense_1 (Dense)             (None, 128)               32896     
                                                                 
 dense_2 (Dense)             (None, 64)                8256      
                                                                 
 dense_3 (Dense)             (None, 32)                2080      
                                                                 
 dense_4 (Dense)             (None, 2)                 66        
                                                                 
=================================================================
Total params: 8,398,050
Trainable params: 8,397,090
Non-trainable params: 960
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history = model_2a.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 486s 8s/step - loss: 0.6483 - accuracy: 0.6344 - val_loss: 0.6356 - val_accuracy: 0.6313
Epoch 2/10
60/60 [==============================] - 657s 11s/step - loss: 0.5740 - accuracy: 0.7201 - val_loss: 0.6000 - val_accuracy: 0.7219
Epoch 3/10
60/60 [==============================] - 660s 11s/step - loss: 0.5566 - accuracy: 0.7180 - val_loss: 0.5627 - val_accuracy: 0.7333
Epoch 4/10
60/60 [==============================] - 662s 11s/step - loss: 0.5311 - accuracy: 0.7398 - val_loss: 0.5487 - val_accuracy: 0.7281
Epoch 5/10
60/60 [==============================] - 664s 11s/step - loss: 0.5235 - accuracy: 0.7396 - val_loss: 0.5421 - val_accuracy: 0.7333
Epoch 6/10
60/60 [==============================] - 577s 10s/step - loss: 0.5029 - accuracy: 0.7560 - val_loss: 0.5343 - val_accuracy: 0.7448
Epoch 7/10
60/60 [==============================] - 470s 8s/step - loss: 0.4948 - accuracy: 0.7560 - val_loss: 0.5312 - val_accuracy: 0.7375
Epoch 8/10
60/60 [==============================] - 514s 9s/step - loss: 0.4863 - accuracy: 0.7633 - val_loss: 0.5305 - val_accuracy: 0.7417
Epoch 9/10
60/60 [==============================] - 514s 9s/step - loss: 0.4691 - accuracy: 0.7812 - val_loss: 0.5332 - val_accuracy: 0.7292


optimizer = Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)

model_3.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"]) 
model_3.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 220, 220, 32)      2432      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 110, 110, 32)     0         
 )                                                               
                                                                 
 conv2d_1 (Conv2D)           (None, 106, 106, 64)      51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 53, 53, 64)       0         
 2D)                                                             
                                                                 
 conv2d_2 (Conv2D)           (None, 51, 51, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 25, 25, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization (BatchN  (None, 25, 25, 128)      512       
 ormalization)                                                   
                                                                 
 conv2d_3 (Conv2D)           (None, 23, 23, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 11, 11, 256)      0         
 2D)                                                             
                                                                 
 dropout (Dropout)           (None, 11, 11, 256)       0         
                                                                 
 flatten (Flatten)           (None, 30976)             0         
                                                                 
 dense (Dense)               (None, 128)               3965056   
                                                                 
 dense_1 (Dense)             (None, 84)                10836     
                                                                 
 dense_2 (Dense)             (None, 42)                3570      
                                                                 
 dense_3 (Dense)             (None, 2)                 86        
                                                                 
=================================================================
Total params: 4,402,780
Trainable params: 4,402,524
Non-trainable params: 256
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history = model_3.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 471s 8s/step - loss: 0.8552 - accuracy: 0.5497 - val_loss: 0.7189 - val_accuracy: 0.5354
Epoch 2/10
60/60 [==============================] - 641s 11s/step - loss: 0.6591 - accuracy: 0.6362 - val_loss: 0.6445 - val_accuracy: 0.6385
Epoch 3/10
60/60 [==============================] - 638s 11s/step - loss: 0.6219 - accuracy: 0.6664 - val_loss: 0.5995 - val_accuracy: 0.6917
Epoch 4/10
60/60 [==============================] - 648s 11s/step - loss: 0.6093 - accuracy: 0.6836 - val_loss: 0.5757 - val_accuracy: 0.7115
Epoch 5/10
60/60 [==============================] - 640s 11s/step - loss: 0.5981 - accuracy: 0.6857 - val_loss: 0.5678 - val_accuracy: 0.6927
Epoch 6/10
60/60 [==============================] - 10691s 181s/step - loss: 0.5924 - accuracy: 0.6971 - val_loss: 0.5605 - val_accuracy: 0.6979
Epoch 7/10
60/60 [==============================] - 439s 7s/step - loss: 0.5731 - accuracy: 0.7094 - val_loss: 0.5578 - val_accuracy: 0.7125
Epoch 8/10
60/60 [==============================] - 450s 8s/step - loss: 0.5682 - accuracy: 0.7185 - val_loss: 0.5504 - val_accuracy: 0.7135
Epoch 9/10
60/60 [==============================] - 450s 8s/step - loss: 0.5679 - accuracy: 0.7164 - val_loss: 0.5503 - val_accuracy: 0.7208
Epoch 10/10
60/60 [==============================] - 487s 8s/step - loss: 0.5656 - accuracy: 0.7125 - val_loss: 0.5520 - val_accuracy: 0.7240


RESULTS = pd.DataFrame()

loss_1, accuracy_1 = model.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model",np.round((loss_1*100),2),np.round((accuracy_1*100),2)]),ignore_index=True)

loss_2, accuracy_2 = model_2.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2",np.round((loss_2*100),2),np.round((accuracy_2*100),2)]),ignore_index=True)

loss_2a, accuracy_2a = model_2a.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2a",np.round((loss_2a*100),2),np.round((accuracy_2a*100),2)]),ignore_index=True)

loss_3, accuracy_3 = model_3.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_3",np.round((loss_3*100),2),np.round((accuracy_3*100),2)]),ignore_index=True)

RESULTS.columns = ['MODEL_NAME','LOSS','ACCURACY']

RESULTS.style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()

38/38 [==============================] - 7s 163ms/step - loss: 0.5908 - accuracy: 0.7142
38/38 [==============================] - 6s 159ms/step - loss: 0.5694 - accuracy: 0.7050
38/38 [==============================] - 25s 656ms/step - loss: 0.5557 - accuracy: 0.7183
38/38 [==============================] - 24s 625ms/step - loss: 0.5746 - accuracy: 0.6983


RESULTS.to_csv("Optimizer_and_loss_finetuning_1.csv",index = False)


optimizer = Adam(learning_rate=0.01)
model.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 109, 109, 32)      4736      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 54, 54, 32)       0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 54, 54, 32)       128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 50, 50, 64)        51264     
                                                                 
 batch_normalization_1 (Batc  (None, 50, 50, 64)       256       
 hNormalization)                                                 
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 25, 25, 64)       0         
 2D)                                                             
                                                                 
 flatten (Flatten)           (None, 40000)             0         
                                                                 
 dense (Dense)               (None, 128)               5120128   
                                                                 
 dense_1 (Dense)             (None, 64)                8256      
                                                                 
 dense_2 (Dense)             (None, 2)                 130       
                                                                 
=================================================================
Total params: 5,184,898
Trainable params: 5,184,706
Non-trainable params: 192
_________________________________________________________________


batch_size = 16
nb_epochs = 10
history = model.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
240/240 [==============================] - 126s 523ms/step - loss: 2.3675 - accuracy: 0.6521 - val_loss: 0.7864 - val_accuracy: 0.6979
Epoch 2/10
240/240 [==============================] - 124s 518ms/step - loss: 0.6270 - accuracy: 0.7078 - val_loss: 0.5919 - val_accuracy: 0.7208
Epoch 3/10
240/240 [==============================] - 124s 515ms/step - loss: 0.5769 - accuracy: 0.7193 - val_loss: 0.6178 - val_accuracy: 0.6854
Epoch 4/10
240/240 [==============================] - 122s 507ms/step - loss: 0.5692 - accuracy: 0.7224 - val_loss: 0.5901 - val_accuracy: 0.7250
Epoch 5/10
240/240 [==============================] - 122s 508ms/step - loss: 0.5425 - accuracy: 0.7297 - val_loss: 0.6170 - val_accuracy: 0.7000
Epoch 6/10
240/240 [==============================] - 123s 514ms/step - loss: 0.5504 - accuracy: 0.7211 - val_loss: 0.6445 - val_accuracy: 0.7073


optimizer = Adam(learning_rate=0.01)
model_2.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model_2.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 109, 109, 32)      4736      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 54, 54, 32)       0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 54, 54, 32)       128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 50, 50, 64)        51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 25, 25, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 25, 25, 64)       256       
 hNormalization)                                                 
                                                                 
 conv2d_2 (Conv2D)           (None, 23, 23, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 11, 11, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 11, 11, 128)      512       
 hNormalization)                                                 
                                                                 
 flatten (Flatten)           (None, 15488)             0         
                                                                 
 dense (Dense)               (None, 128)               1982592   
                                                                 
 dense_1 (Dense)             (None, 84)                10836     
                                                                 
 dense_2 (Dense)             (None, 42)                3570      
                                                                 
 dense_3 (Dense)             (None, 2)                 86        
                                                                 
=================================================================
Total params: 2,127,836
Trainable params: 2,127,388
Non-trainable params: 448
_________________________________________________________________


batch_size = 16
nb_epochs = 10
history = model_2.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
240/240 [==============================] - 120s 496ms/step - loss: 0.9150 - accuracy: 0.5143 - val_loss: 0.6935 - val_accuracy: 0.5000
Epoch 2/10
240/240 [==============================] - 116s 485ms/step - loss: 0.6941 - accuracy: 0.5042 - val_loss: 0.6933 - val_accuracy: 0.5000
Epoch 3/10
240/240 [==============================] - 115s 479ms/step - loss: 0.6934 - accuracy: 0.4930 - val_loss: 0.6931 - val_accuracy: 0.5000


optimizer = Adam(learning_rate=0.01)
model_2a.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model_2a.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 220, 220, 32)      2432      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 110, 110, 32)     0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 110, 110, 32)     128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 106, 106, 64)      51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 53, 53, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 53, 53, 64)       256       
 hNormalization)                                                 
                                                                 
 conv2d_2 (Conv2D)           (None, 51, 51, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 25, 25, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 25, 25, 128)      512       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 25, 25, 128)       0         
                                                                 
 conv2d_3 (Conv2D)           (None, 23, 23, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 11, 11, 256)      0         
 2D)                                                             
                                                                 
 batch_normalization_3 (Batc  (None, 11, 11, 256)      1024      
 hNormalization)                                                 
                                                                 
 dropout_1 (Dropout)         (None, 11, 11, 256)       0         
                                                                 
 flatten (Flatten)           (None, 30976)             0         
                                                                 
 dense (Dense)               (None, 256)               7930112   
                                                                 
 dropout_2 (Dropout)         (None, 256)               0         
                                                                 
 dense_1 (Dense)             (None, 128)               32896     
                                                                 
 dense_2 (Dense)             (None, 64)                8256      
                                                                 
 dense_3 (Dense)             (None, 32)                2080      
                                                                 
 dense_4 (Dense)             (None, 2)                 66        
                                                                 
=================================================================
Total params: 8,398,050
Trainable params: 8,397,090
Non-trainable params: 960
_________________________________________________________________


batch_size = 16
nb_epochs = 10
history = model_2a.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
240/240 [==============================] - 443s 2s/step - loss: 0.9402 - accuracy: 0.6276 - val_loss: 0.5802 - val_accuracy: 0.6969
Epoch 2/10
240/240 [==============================] - 443s 2s/step - loss: 0.6269 - accuracy: 0.6826 - val_loss: 0.6670 - val_accuracy: 0.5906
Epoch 3/10
240/240 [==============================] - 441s 2s/step - loss: 0.6524 - accuracy: 0.6266 - val_loss: 0.6493 - val_accuracy: 0.5000


optimizer = Adam(learning_rate=0.01)
model_3.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model_3.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 220, 220, 32)      2432      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 110, 110, 32)     0         
 )                                                               
                                                                 
 conv2d_1 (Conv2D)           (None, 106, 106, 64)      51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 53, 53, 64)       0         
 2D)                                                             
                                                                 
 conv2d_2 (Conv2D)           (None, 51, 51, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 25, 25, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization (BatchN  (None, 25, 25, 128)      512       
 ormalization)                                                   
                                                                 
 conv2d_3 (Conv2D)           (None, 23, 23, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 11, 11, 256)      0         
 2D)                                                             
                                                                 
 dropout (Dropout)           (None, 11, 11, 256)       0         
                                                                 
 flatten (Flatten)           (None, 30976)             0         
                                                                 
 dense (Dense)               (None, 128)               3965056   
                                                                 
 dense_1 (Dense)             (None, 84)                10836     
                                                                 
 dense_2 (Dense)             (None, 42)                3570      
                                                                 
 dense_3 (Dense)             (None, 2)                 86        
                                                                 
=================================================================
Total params: 4,402,780
Trainable params: 4,402,524
Non-trainable params: 256
_________________________________________________________________


batch_size = 16
nb_epochs = 10
history = model_3.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
240/240 [==============================] - 417s 2s/step - loss: 0.9298 - accuracy: 0.5000 - val_loss: 0.6933 - val_accuracy: 0.5000
Epoch 2/10
240/240 [==============================] - 406s 2s/step - loss: 0.6936 - accuracy: 0.4844 - val_loss: 0.6932 - val_accuracy: 0.5000
Epoch 3/10
240/240 [==============================] - 405s 2s/step - loss: 0.6935 - accuracy: 0.4969 - val_loss: 0.6933 - val_accuracy: 0.5000


RESULTS = pd.DataFrame()

loss_1, accuracy_1 = model.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model",np.round((loss_1*100),2),np.round((accuracy_1*100),2)]),ignore_index=True)

loss_2, accuracy_2 = model_2.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2",np.round((loss_2*100),2),np.round((accuracy_2*100),2)]),ignore_index=True)

loss_2a, accuracy_2a = model_2a.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2a",np.round((loss_2a*100),2),np.round((accuracy_2a*100),2)]),ignore_index=True)

loss_3, accuracy_3 = model_3.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_3",np.round((loss_3*100),2),np.round((accuracy_3*100),2)]),ignore_index=True)

RESULTS.columns = ['MODEL_NAME','LOSS','ACCURACY']

RESULTS.style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()

38/38 [==============================] - 6s 150ms/step - loss: 0.6382 - accuracy: 0.6917
38/38 [==============================] - 6s 141ms/step - loss: 0.6931 - accuracy: 0.5000
38/38 [==============================] - 22s 572ms/step - loss: 0.6493 - accuracy: 0.5000
38/38 [==============================] - 21s 553ms/step - loss: 0.6933 - accuracy: 0.5000


RESULTS.to_csv("Reduce_Batch_Size.csv",index = False)


batch_size = 120
nb_epochs = 10
history = model.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
32/32 [==============================] - 106s 3s/step - loss: 0.4911 - accuracy: 0.7555 - val_loss: 0.5871 - val_accuracy: 0.7240
Epoch 2/10
32/32 [==============================] - 103s 3s/step - loss: 0.4547 - accuracy: 0.7755 - val_loss: 0.6005 - val_accuracy: 0.7250
Epoch 3/10
32/32 [==============================] - 104s 3s/step - loss: 0.4403 - accuracy: 0.7823 - val_loss: 0.6144 - val_accuracy: 0.7208


batch_size = 120
nb_epochs = 10
history = model_2.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
32/32 [==============================] - 110s 3s/step - loss: 0.6930 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000
Epoch 2/10
32/32 [==============================] - 110s 3s/step - loss: 0.6930 - accuracy: 0.5000 - val_loss: 0.6931 - val_accuracy: 0.5000
Epoch 3/10
32/32 [==============================] - 110s 3s/step - loss: 0.6930 - accuracy: 0.5000 - val_loss: 0.6932 - val_accuracy: 0.5000


batch_size = 120
nb_epochs = 10
history = model_2a.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
32/32 [==============================] - 445s 14s/step - loss: 0.6361 - accuracy: 0.6518 - val_loss: 0.6157 - val_accuracy: 0.7073
Epoch 2/10
32/32 [==============================] - 466s 15s/step - loss: 0.6189 - accuracy: 0.7026 - val_loss: 0.5929 - val_accuracy: 0.7167
Epoch 3/10
32/32 [==============================] - 463s 14s/step - loss: 0.5974 - accuracy: 0.7227 - val_loss: 0.5850 - val_accuracy: 0.7250
Epoch 4/10
32/32 [==============================] - 485s 15s/step - loss: 0.5821 - accuracy: 0.7279 - val_loss: 0.5795 - val_accuracy: 0.7302
Epoch 5/10
32/32 [==============================] - 11978s 20s/step - loss: 0.5732 - accuracy: 0.7279 - val_loss: 0.5754 - val_accuracy: 0.7240
Epoch 6/10
32/32 [==============================] - 583s 18s/step - loss: 0.5696 - accuracy: 0.7266 - val_loss: 0.5779 - val_accuracy: 0.7135
Epoch 7/10
32/32 [==============================] - 584s 18s/step - loss: 0.5621 - accuracy: 0.7388 - val_loss: 0.5658 - val_accuracy: 0.7302
Epoch 8/10
32/32 [==============================] - 520s 15s/step - loss: 0.5561 - accuracy: 0.7362 - val_loss: 0.5506 - val_accuracy: 0.7437
Epoch 9/10
32/32 [==============================] - 403s 13s/step - loss: 0.5498 - accuracy: 0.7375 - val_loss: 0.5499 - val_accuracy: 0.7333
Epoch 10/10
32/32 [==============================] - 403s 13s/step - loss: 0.5399 - accuracy: 0.7495 - val_loss: 0.5484 - val_accuracy: 0.7365


batch_size = 120
nb_epochs = 10
history = model_3.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
32/32 [==============================] - 387s 12s/step - loss: 0.6933 - accuracy: 0.5000 - val_loss: 0.6932 - val_accuracy: 0.5000
Epoch 2/10
32/32 [==============================] - 391s 12s/step - loss: 0.6932 - accuracy: 0.5000 - val_loss: 0.6932 - val_accuracy: 0.5000
Epoch 3/10
32/32 [==============================] - 393s 12s/step - loss: 0.6934 - accuracy: 0.4839 - val_loss: 0.6932 - val_accuracy: 0.5000


RESULTS = pd.DataFrame()

loss_1, accuracy_1 = model.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model",np.round((loss_1*100),2),np.round((accuracy_1*100),2)]),ignore_index=True)

loss_2, accuracy_2 = model_2.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2",np.round((loss_2*100),2),np.round((accuracy_2*100),2)]),ignore_index=True)

loss_2a, accuracy_2a = model_2a.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2a",np.round((loss_2a*100),2),np.round((accuracy_2a*100),2)]),ignore_index=True)

loss_3, accuracy_3 = model_3.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_3",np.round((loss_3*100),2),np.round((accuracy_3*100),2)]),ignore_index=True)

RESULTS.columns = ['MODEL_NAME','LOSS','ACCURACY']

RESULTS.style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()

38/38 [==============================] - 35s 910ms/step - loss: 0.6109 - accuracy: 0.7150
38/38 [==============================] - 25s 658ms/step - loss: 0.6932 - accuracy: 0.5000
38/38 [==============================] - 40s 1s/step - loss: 0.5626 - accuracy: 0.7158
38/38 [==============================] - 34s 905ms/step - loss: 0.6932 - accuracy: 0.5000


RESULTS.to_csv("Increase_Batch_Size.csv",index = False)


tf.keras.backend.clear_session()

# Initialize the model
model = Sequential()

# Add a Convolutional Layer with 32 filters of size 7X7, strides(2,2) and activation function as 'relu' 
model.add(Conv2D(filters=32, kernel_size=3, strides=(1, 1), padding='same', kernel_regularizer = tf.keras.regularizers.l2(0.01), activity_regularizer = tf.keras.regularizers.l1(0.01), activation="relu", input_shape=(224, 224, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())

# model.add(Dropout(0.3))

# Add a Convolutional Layer with 64 filters of size 5X5, strides(2,2) and activation function as 'relu' 
model.add(Conv2D(filters=64, kernel_size=3, strides=(1, 1), padding='same', kernel_regularizer = tf.keras.regularizers.l2(0.01), activity_regularizer = tf.keras.regularizers.l1(0.01), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(BatchNormalization())

model.add(Dropout(0.3))

# Flatten the layer
model.add(Flatten())

# Add Fully Connected Layer with 128 units and activation function as 'relu'
model.add(Dense(128, activation="relu"))
model.add(Dense(64, activation="relu"))

#Add Fully Connected Layer with 3 units and activation function as 'softmax'
model.add(Dense(2, activation="sigmoid"))


optimizer = Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
model.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 224, 224, 32)      896       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 112, 112, 32)     0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 112, 112, 32)     128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 112, 112, 64)      18496     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 56, 56, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 56, 56, 64)       256       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 56, 56, 64)        0         
                                                                 
 flatten (Flatten)           (None, 200704)            0         
                                                                 
 dense (Dense)               (None, 128)               25690240  
                                                                 
 dense_1 (Dense)             (None, 64)                8256      
                                                                 
 dense_2 (Dense)             (None, 2)                 130       
                                                                 
=================================================================
Total params: 25,718,402
Trainable params: 25,718,210
Non-trainable params: 192
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history = model.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 258s 4s/step - loss: 373833.3125 - accuracy: 0.6779 - val_loss: 367506.0000 - val_accuracy: 0.7052
Epoch 2/10
60/60 [==============================] - 248s 4s/step - loss: 359018.6562 - accuracy: 0.7563 - val_loss: 352139.3750 - val_accuracy: 0.7198
Epoch 3/10
60/60 [==============================] - 247s 4s/step - loss: 344620.0000 - accuracy: 0.8026 - val_loss: 337732.8438 - val_accuracy: 0.7312
Epoch 4/10
60/60 [==============================] - 284s 5s/step - loss: 330597.5625 - accuracy: 0.8349 - val_loss: 323821.8750 - val_accuracy: 0.7010
Epoch 5/10
60/60 [==============================] - 289s 5s/step - loss: 316935.0000 - accuracy: 0.8841 - val_loss: 310343.5938 - val_accuracy: 0.7208
Epoch 6/10
60/60 [==============================] - 284s 5s/step - loss: 303653.0000 - accuracy: 0.9201 - val_loss: 297248.7188 - val_accuracy: 0.7260
Epoch 7/10
60/60 [==============================] - 286s 5s/step - loss: 290755.3438 - accuracy: 0.9451 - val_loss: 284523.7812 - val_accuracy: 0.7385
Epoch 8/10
60/60 [==============================] - 286s 5s/step - loss: 278221.6250 - accuracy: 0.9677 - val_loss: 272167.8750 - val_accuracy: 0.7063
Epoch 9/10
60/60 [==============================] - 287s 5s/step - loss: 266036.9375 - accuracy: 0.9828 - val_loss: 260131.4688 - val_accuracy: 0.7094
Epoch 10/10
60/60 [==============================] - 288s 5s/step - loss: 254169.2812 - accuracy: 0.9909 - val_loss: 248407.6406 - val_accuracy: 0.7104


tf.keras.backend.clear_session()

# second model
model_2 = Sequential()

model_2.add(Conv2D(filters=32, kernel_size=7, strides=(2, 2), padding='same', kernel_regularizer = tf.keras.regularizers.l2(0.01), activity_regularizer = tf.keras.regularizers.l1(0.01), activation="relu", input_shape=(224, 224, 3)))
model_2.add(MaxPooling2D(pool_size=(2, 2)))
model_2.add(BatchNormalization())

model_2.add(Conv2D(filters=64, kernel_size=5, strides=(1, 1), padding='same', kernel_regularizer = tf.keras.regularizers.l2(0.01), activity_regularizer = tf.keras.regularizers.l1(0.01), activation="relu"))
model_2.add(MaxPooling2D(pool_size=(2, 2)))
model_2.add(BatchNormalization())
model_2.add(Dropout(rate=0.3))

model_2.add(Conv2D(filters=128, kernel_size=3, strides=(1, 1), padding='same', kernel_regularizer = tf.keras.regularizers.l2(0.01), activity_regularizer = tf.keras.regularizers.l1(0.01), activation="relu"))
model_2.add(MaxPooling2D(pool_size=(2, 2)))
model_2.add(BatchNormalization())
# model_2.add(Dropout(rate=0.7))

model_2.add(Flatten())

model_2.add(Dense(128, kernel_initializer='he_normal', activation="relu"))
# model_2.add(BatchNormalization())
# model_2.add(Dropout(rate=0.5))

model_2.add(Dense(84, kernel_initializer='he_normal', activation="relu"))
# model_2.add(BatchNormalization())
model_2.add(Dense(42, kernel_initializer='he_normal', activation="relu"))

model_2.add(Dense(2, activation="sigmoid"))


optimizer = Adam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
model_2.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model_2.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 112, 112, 32)      4736      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 56, 56, 32)       0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 56, 56, 32)       128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 56, 56, 64)        51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 28, 28, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 28, 28, 64)       256       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 28, 28, 64)        0         
                                                                 
 conv2d_2 (Conv2D)           (None, 28, 28, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 14, 14, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 14, 14, 128)      512       
 hNormalization)                                                 
                                                                 
 flatten (Flatten)           (None, 25088)             0         
                                                                 
 dense (Dense)               (None, 128)               3211392   
                                                                 
 dense_1 (Dense)             (None, 84)                10836     
                                                                 
 dense_2 (Dense)             (None, 42)                3570      
                                                                 
 dense_3 (Dense)             (None, 2)                 86        
                                                                 
=================================================================
Total params: 3,356,636
Trainable params: 3,356,188
Non-trainable params: 448
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history_2 = model_2.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 108s 2s/step - loss: 23017.2090 - accuracy: 0.6469 - val_loss: 2748.1780 - val_accuracy: 0.6313
Epoch 2/10
60/60 [==============================] - 108s 2s/step - loss: 1179.1445 - accuracy: 0.6654 - val_loss: 747.2348 - val_accuracy: 0.5844
Epoch 3/10
60/60 [==============================] - 126s 2s/step - loss: 612.2598 - accuracy: 0.6789 - val_loss: 622.0334 - val_accuracy: 0.5135
Epoch 4/10
60/60 [==============================] - 123s 2s/step - loss: 437.5329 - accuracy: 0.6924 - val_loss: 527.3038 - val_accuracy: 0.4990
Epoch 5/10
60/60 [==============================] - 123s 2s/step - loss: 313.8621 - accuracy: 0.6844 - val_loss: 431.1918 - val_accuracy: 0.5219
Epoch 6/10
60/60 [==============================] - 123s 2s/step - loss: 236.2792 - accuracy: 0.6909 - val_loss: 341.1299 - val_accuracy: 0.5000
Epoch 7/10
60/60 [==============================] - 123s 2s/step - loss: 189.0528 - accuracy: 0.6961 - val_loss: 259.9745 - val_accuracy: 0.5000
Epoch 8/10
60/60 [==============================] - 123s 2s/step - loss: 157.5050 - accuracy: 0.7008 - val_loss: 186.4092 - val_accuracy: 0.5000
Epoch 9/10
60/60 [==============================] - 123s 2s/step - loss: 135.0659 - accuracy: 0.7016 - val_loss: 132.1451 - val_accuracy: 0.5010
Epoch 10/10
60/60 [==============================] - 123s 2s/step - loss: 117.9370 - accuracy: 0.7104 - val_loss: 112.4869 - val_accuracy: 0.5094


tf.keras.backend.clear_session()

# second model
model_2a = Sequential()

model_2a.add(Conv2D(filters=32, kernel_size=5, strides=(1, 1), kernel_regularizer = tf.keras.regularizers.l2(0.01), activity_regularizer = tf.keras.regularizers.l1(0.01), activation="relu", input_shape=(224, 224, 3)))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Conv2D(filters=64, kernel_size=5, strides=(1, 1), kernel_regularizer = tf.keras.regularizers.l2(0.01), activity_regularizer = tf.keras.regularizers.l1(0.01), activation="relu"))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Conv2D(filters=128, kernel_size=3, strides=(1, 1), kernel_regularizer = tf.keras.regularizers.l2(0.01), activity_regularizer = tf.keras.regularizers.l1(0.01), activation="relu"))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Dropout(rate=0.2))

model_2a.add(Conv2D(filters=256, kernel_size=3, strides=(1, 1), activation="relu"))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Dropout(rate=0.2))

model_2a.add(Flatten())

model_2a.add(Dense(256, kernel_initializer='he_normal', activation="relu"))
model_2a.add(Dropout(rate=0.25))

model_2a.add(Dense(128, kernel_initializer='he_normal', activation="relu"))

model_2a.add(Dense(64, kernel_initializer='he_normal', activation="relu"))

model_2a.add(Dense(32, kernel_initializer='he_normal', activation="relu"))

model_2a.add(Dense(2, activation="sigmoid"))


optimizer = Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
model_2a.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model_2a.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 220, 220, 32)      2432      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 110, 110, 32)     0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 110, 110, 32)     128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 106, 106, 64)      51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 53, 53, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 53, 53, 64)       256       
 hNormalization)                                                 
                                                                 
 conv2d_2 (Conv2D)           (None, 51, 51, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 25, 25, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 25, 25, 128)      512       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 25, 25, 128)       0         
                                                                 
 conv2d_3 (Conv2D)           (None, 23, 23, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 11, 11, 256)      0         
 2D)                                                             
                                                                 
 batch_normalization_3 (Batc  (None, 11, 11, 256)      1024      
 hNormalization)                                                 
                                                                 
 dropout_1 (Dropout)         (None, 11, 11, 256)       0         
                                                                 
 flatten (Flatten)           (None, 30976)             0         
                                                                 
 dense (Dense)               (None, 256)               7930112   
                                                                 
 dropout_2 (Dropout)         (None, 256)               0         
                                                                 
 dense_1 (Dense)             (None, 128)               32896     
                                                                 
 dense_2 (Dense)             (None, 64)                8256      
                                                                 
 dense_3 (Dense)             (None, 32)                2080      
                                                                 
 dense_4 (Dense)             (None, 2)                 66        
                                                                 
=================================================================
Total params: 8,398,050
Trainable params: 8,397,090
Non-trainable params: 960
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history_2a = model_2a.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 365s 6s/step - loss: 313694.4062 - accuracy: 0.6214 - val_loss: 296763.9062 - val_accuracy: 0.5875
Epoch 2/10
60/60 [==============================] - 360s 6s/step - loss: 277786.9688 - accuracy: 0.6849 - val_loss: 260931.8281 - val_accuracy: 0.7052
Epoch 3/10
60/60 [==============================] - 351s 6s/step - loss: 244889.1719 - accuracy: 0.6930 - val_loss: 229189.9531 - val_accuracy: 0.7229
Epoch 4/10
60/60 [==============================] - 397s 7s/step - loss: 214784.2812 - accuracy: 0.7044 - val_loss: 200388.6719 - val_accuracy: 0.6802
Epoch 5/10
60/60 [==============================] - 395s 7s/step - loss: 187482.0625 - accuracy: 0.7083 - val_loss: 174608.1875 - val_accuracy: 0.6812
Epoch 6/10
60/60 [==============================] - 393s 7s/step - loss: 163200.0625 - accuracy: 0.7107 - val_loss: 151705.8906 - val_accuracy: 0.6927
Epoch 7/10
60/60 [==============================] - 394s 7s/step - loss: 141574.5781 - accuracy: 0.7141 - val_loss: 131268.6719 - val_accuracy: 0.7156
Epoch 8/10
60/60 [==============================] - 393s 7s/step - loss: 122353.3906 - accuracy: 0.7148 - val_loss: 113170.2734 - val_accuracy: 0.7052
Epoch 9/10
60/60 [==============================] - 394s 7s/step - loss: 105394.9766 - accuracy: 0.7240 - val_loss: 97349.0547 - val_accuracy: 0.7125
Epoch 10/10
60/60 [==============================] - 394s 7s/step - loss: 90617.5547 - accuracy: 0.7250 - val_loss: 83556.2266 - val_accuracy: 0.7073


tf.keras.backend.clear_session()

# second model
model_3 = Sequential()

model_3.add(Conv2D(filters=32, kernel_size=3, strides=(1, 1), padding='same', kernel_initializer='he_normal', activation="relu", input_shape=(224, 224, 3)))
model_3.add(MaxPooling2D(pool_size=(2, 2)))
model_3.add(BatchNormalization())

model_3.add(Conv2D(filters=64, kernel_size=3, strides=(1, 1), padding='same', kernel_initializer='he_normal', activation="relu"))
model_3.add(MaxPooling2D(pool_size=(2, 2)))
model_3.add(BatchNormalization())

model_3.add(Dropout(rate=0.35))

model_3.add(Conv2D(filters=128, kernel_size=3, strides=(1, 1), padding='same', kernel_initializer='he_normal', activation="relu"))
model_3.add(MaxPooling2D(pool_size=(2, 2)))
model_3.add(BatchNormalization())

model_3.add(Conv2D(filters=256, kernel_size=3, strides=(1, 1), padding='same', kernel_initializer='he_normal', activation="relu"))
model_3.add(MaxPooling2D(pool_size=(2, 2)))
model_3.add(BatchNormalization())

# model_3.add(Dropout(rate=0.35))

model_3.add(Flatten())

# model_3.add(Dense(624, kernel_initializer='he_normal', kernel_regularizer = tf.keras.regularizers.l2(0.01), activity_regularizer = tf.keras.regularizers.l1(0.01), activation="relu"))
model_3.add(Dense(824, kernel_initializer='he_normal', activation="relu"))
# model_3.add(Dense(512, kernel_initializer='he_normal', kernel_regularizer = tf.keras.regularizers.l2(0.01), activity_regularizer = tf.keras.regularizers.l1(0.01), activation="relu"))
# model_3.add(Dense(512, kernel_initializer='he_normal', activation="relu"))
# model_3.add(Dense(300, kernel_initializer='he_normal', activation="relu"))

model_3.add(Dense(84, kernel_initializer='he_normal', activation="relu"))

model_3.add(Dense(42, kernel_initializer='he_normal', activation="relu"))

model_3.add(Dropout(rate=0.35))

model_3.add(Dense(2, activation="sigmoid"))


optimizer = Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
model_3.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model_3.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 224, 224, 32)      896       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 112, 112, 32)     0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 112, 112, 32)     128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 112, 112, 64)      18496     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 56, 56, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 56, 56, 64)       256       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 56, 56, 64)        0         
                                                                 
 conv2d_2 (Conv2D)           (None, 56, 56, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 28, 28, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 28, 28, 128)      512       
 hNormalization)                                                 
                                                                 
 conv2d_3 (Conv2D)           (None, 28, 28, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 14, 14, 256)      0         
 2D)                                                             
                                                                 
 batch_normalization_3 (Batc  (None, 14, 14, 256)      1024      
 hNormalization)                                                 
                                                                 
 flatten (Flatten)           (None, 50176)             0         
                                                                 
 dense (Dense)               (None, 824)               41345848  
                                                                 
 dense_1 (Dense)             (None, 84)                69300     
                                                                 
 dense_2 (Dense)             (None, 42)                3570      
                                                                 
 dropout_1 (Dropout)         (None, 42)                0         
                                                                 
 dense_3 (Dense)             (None, 2)                 86        
                                                                 
=================================================================
Total params: 41,809,140
Trainable params: 41,808,180
Non-trainable params: 960
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history_3 = model_3.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 284s 5s/step - loss: 0.6689 - accuracy: 0.6477 - val_loss: 0.6505 - val_accuracy: 0.6812
Epoch 2/10
60/60 [==============================] - 308s 5s/step - loss: 0.5731 - accuracy: 0.7156 - val_loss: 0.5848 - val_accuracy: 0.7177
Epoch 3/10
60/60 [==============================] - 308s 5s/step - loss: 0.5438 - accuracy: 0.7365 - val_loss: 0.5653 - val_accuracy: 0.7198
Epoch 4/10
60/60 [==============================] - 321s 5s/step - loss: 0.5135 - accuracy: 0.7565 - val_loss: 0.5697 - val_accuracy: 0.7219
Epoch 5/10
60/60 [==============================] - 388s 6s/step - loss: 0.4840 - accuracy: 0.7763 - val_loss: 0.5680 - val_accuracy: 0.7208


RESULTS = pd.DataFrame()

loss_1, accuracy_1 = model.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model",np.round((loss_1*100),2),np.round((accuracy_1*100),2)]),ignore_index=True)

loss_2, accuracy_2 = model_2.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2",np.round((loss_2*100),2),np.round((accuracy_2*100),2)]),ignore_index=True)

loss_2a, accuracy_2a = model_2a.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2a",np.round((loss_2a*100),2),np.round((accuracy_2a*100),2)]),ignore_index=True)

loss_3, accuracy_3 = model_3.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_3",np.round((loss_3*100),2),np.round((accuracy_3*100),2)]),ignore_index=True)

RESULTS.columns = ['MODEL_NAME','LOSS','ACCURACY']

RESULTS.style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()

38/38 [==============================] - 21s 531ms/step - loss: 248510.5312 - accuracy: 0.6867
38/38 [==============================] - 10s 257ms/step - loss: 107.0487 - accuracy: 0.5067
38/38 [==============================] - 30s 773ms/step - loss: 83549.0156 - accuracy: 0.6992
38/38 [==============================] - 23s 603ms/step - loss: 0.5760 - accuracy: 0.7067


RESULTS.to_csv("Add_Update_Model_Layers.csv",index = False)


optimizer = RMSprop(learning_rate=0.00001, rho=0.9, momentum=0.9, epsilon=1e-07)
model.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 224, 224, 32)      896       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 112, 112, 32)     0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 112, 112, 32)     128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 112, 112, 64)      18496     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 56, 56, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 56, 56, 64)       256       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 56, 56, 64)        0         
                                                                 
 flatten (Flatten)           (None, 200704)            0         
                                                                 
 dense (Dense)               (None, 128)               25690240  
                                                                 
 dense_1 (Dense)             (None, 64)                8256      
                                                                 
 dense_2 (Dense)             (None, 2)                 130       
                                                                 
=================================================================
Total params: 25,718,402
Trainable params: 25,718,210
Non-trainable params: 192
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history = model.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 446s 7s/step - loss: 194680.3594 - accuracy: 0.8076 - val_loss: 143168.9531 - val_accuracy: 0.6875
Epoch 2/10
60/60 [==============================] - 394s 7s/step - loss: 108535.7344 - accuracy: 0.8375 - val_loss: 78112.7812 - val_accuracy: 0.5594
Epoch 3/10
60/60 [==============================] - 386s 6s/step - loss: 57126.1641 - accuracy: 0.8503 - val_loss: 39935.3047 - val_accuracy: 0.5979
Epoch 4/10
60/60 [==============================] - 376s 6s/step - loss: 29403.8457 - accuracy: 0.8768 - val_loss: 21068.7168 - val_accuracy: 0.6375
Epoch 5/10
60/60 [==============================] - 384s 6s/step - loss: 13762.1348 - accuracy: 0.8974 - val_loss: 9990.2607 - val_accuracy: 0.5000
Epoch 6/10
60/60 [==============================] - 382s 6s/step - loss: 4994.7144 - accuracy: 0.9122 - val_loss: 5183.1089 - val_accuracy: 0.5000
Epoch 7/10
60/60 [==============================] - 395s 7s/step - loss: 1362.1005 - accuracy: 0.8971 - val_loss: 3233.0425 - val_accuracy: 0.5000
Epoch 8/10
60/60 [==============================] - 397s 7s/step - loss: 206.5308 - accuracy: 0.8964 - val_loss: 2365.9502 - val_accuracy: 0.5000
Epoch 9/10
60/60 [==============================] - 397s 7s/step - loss: 64.5132 - accuracy: 0.8818 - val_loss: 1478.6406 - val_accuracy: 0.5000
Epoch 10/10
60/60 [==============================] - 396s 7s/step - loss: 32.3362 - accuracy: 0.8581 - val_loss: 280.6079 - val_accuracy: 0.5000


optimizer = RMSprop(learning_rate=0.00001, rho=0.9, momentum=0.9, epsilon=1e-07)
model_2.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model_2.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 112, 112, 32)      4736      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 56, 56, 32)       0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 56, 56, 32)       128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 56, 56, 64)        51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 28, 28, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 28, 28, 64)       256       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 28, 28, 64)        0         
                                                                 
 conv2d_2 (Conv2D)           (None, 28, 28, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 14, 14, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 14, 14, 128)      512       
 hNormalization)                                                 
                                                                 
 flatten (Flatten)           (None, 25088)             0         
                                                                 
 dense (Dense)               (None, 128)               3211392   
                                                                 
 dense_1 (Dense)             (None, 84)                10836     
                                                                 
 dense_2 (Dense)             (None, 42)                3570      
                                                                 
 dense_3 (Dense)             (None, 2)                 86        
                                                                 
=================================================================
Total params: 3,356,636
Trainable params: 3,356,188
Non-trainable params: 448
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history_2 = model_2.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 162s 3s/step - loss: 45.5135 - accuracy: 0.6370 - val_loss: 17.6134 - val_accuracy: 0.5146
Epoch 2/10
60/60 [==============================] - 160s 3s/step - loss: 12.8880 - accuracy: 0.5891 - val_loss: 6.5359 - val_accuracy: 0.5010
Epoch 3/10
60/60 [==============================] - 160s 3s/step - loss: 6.4028 - accuracy: 0.5654 - val_loss: 3.6476 - val_accuracy: 0.4979
Epoch 4/10
60/60 [==============================] - 174s 3s/step - loss: 4.1662 - accuracy: 0.5719 - val_loss: 2.7279 - val_accuracy: 0.5042
Epoch 5/10
60/60 [==============================] - 177s 3s/step - loss: 3.1581 - accuracy: 0.5792 - val_loss: 2.3110 - val_accuracy: 0.5042
Epoch 6/10
60/60 [==============================] - 177s 3s/step - loss: 2.5679 - accuracy: 0.5659 - val_loss: 2.0630 - val_accuracy: 0.5042
Epoch 7/10
60/60 [==============================] - 177s 3s/step - loss: 2.1788 - accuracy: 0.5648 - val_loss: 1.8385 - val_accuracy: 0.5052
Epoch 8/10
60/60 [==============================] - 177s 3s/step - loss: 1.8797 - accuracy: 0.5539 - val_loss: 1.6489 - val_accuracy: 0.5052
Epoch 9/10
60/60 [==============================] - 177s 3s/step - loss: 1.6528 - accuracy: 0.5508 - val_loss: 1.4988 - val_accuracy: 0.5031
Epoch 10/10
60/60 [==============================] - 177s 3s/step - loss: 1.4750 - accuracy: 0.5549 - val_loss: 1.3631 - val_accuracy: 0.5042


optimizer = RMSprop(learning_rate=0.00001, rho=0.9, momentum=0.9, epsilon=1e-07)
model_2a.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model_2a.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 220, 220, 32)      2432      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 110, 110, 32)     0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 110, 110, 32)     128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 106, 106, 64)      51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 53, 53, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 53, 53, 64)       256       
 hNormalization)                                                 
                                                                 
 conv2d_2 (Conv2D)           (None, 51, 51, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 25, 25, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 25, 25, 128)      512       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 25, 25, 128)       0         
                                                                 
 conv2d_3 (Conv2D)           (None, 23, 23, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 11, 11, 256)      0         
 2D)                                                             
                                                                 
 batch_normalization_3 (Batc  (None, 11, 11, 256)      1024      
 hNormalization)                                                 
                                                                 
 dropout_1 (Dropout)         (None, 11, 11, 256)       0         
                                                                 
 flatten (Flatten)           (None, 30976)             0         
                                                                 
 dense (Dense)               (None, 256)               7930112   
                                                                 
 dropout_2 (Dropout)         (None, 256)               0         
                                                                 
 dense_1 (Dense)             (None, 128)               32896     
                                                                 
 dense_2 (Dense)             (None, 64)                8256      
                                                                 
 dense_3 (Dense)             (None, 32)                2080      
                                                                 
 dense_4 (Dense)             (None, 2)                 66        
                                                                 
=================================================================
Total params: 8,398,050
Trainable params: 8,397,090
Non-trainable params: 960
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history_2a = model_2a.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 542s 9s/step - loss: 37686.1016 - accuracy: 0.6768 - val_loss: 9170.2979 - val_accuracy: 0.5635
Epoch 2/10
60/60 [==============================] - 567s 9s/step - loss: 2617.6323 - accuracy: 0.6745 - val_loss: 5595.0859 - val_accuracy: 0.5000
Epoch 3/10
60/60 [==============================] - 564s 9s/step - loss: 416.0942 - accuracy: 0.6419 - val_loss: 7352.5571 - val_accuracy: 0.5000
Epoch 4/10
60/60 [==============================] - 529s 9s/step - loss: 140.3253 - accuracy: 0.5997 - val_loss: 7707.4409 - val_accuracy: 0.5000


optimizer = RMSprop(learning_rate=0.00001, rho=0.9, momentum=0.9, epsilon=1e-07)
model_3.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model_3.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 224, 224, 32)      896       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 112, 112, 32)     0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 112, 112, 32)     128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 112, 112, 64)      18496     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 56, 56, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 56, 56, 64)       256       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 56, 56, 64)        0         
                                                                 
 conv2d_2 (Conv2D)           (None, 56, 56, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 28, 28, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 28, 28, 128)      512       
 hNormalization)                                                 
                                                                 
 conv2d_3 (Conv2D)           (None, 28, 28, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 14, 14, 256)      0         
 2D)                                                             
                                                                 
 batch_normalization_3 (Batc  (None, 14, 14, 256)      1024      
 hNormalization)                                                 
                                                                 
 flatten (Flatten)           (None, 50176)             0         
                                                                 
 dense (Dense)               (None, 824)               41345848  
                                                                 
 dense_1 (Dense)             (None, 84)                69300     
                                                                 
 dense_2 (Dense)             (None, 42)                3570      
                                                                 
 dropout_1 (Dropout)         (None, 42)                0         
                                                                 
 dense_3 (Dense)             (None, 2)                 86        
                                                                 
=================================================================
Total params: 41,809,140
Trainable params: 41,808,180
Non-trainable params: 960
_________________________________________________________________


batch_size = 64
nb_epochs = 10
history_3 = model_3.fit(X_train1, y_train1,
                    batch_size=batch_size,
                    epochs=nb_epochs, 
                    validation_data=(X_val, y_val),
                    initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
60/60 [==============================] - 456s 8s/step - loss: 0.6022 - accuracy: 0.7229 - val_loss: 0.5698 - val_accuracy: 0.7208
Epoch 2/10
60/60 [==============================] - 452s 8s/step - loss: 0.5490 - accuracy: 0.7518 - val_loss: 0.5737 - val_accuracy: 0.7208
Epoch 3/10
60/60 [==============================] - 453s 8s/step - loss: 0.4865 - accuracy: 0.7812 - val_loss: 0.5845 - val_accuracy: 0.7208


RESULTS = pd.DataFrame()

loss_1, accuracy_1 = model.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model",np.round((loss_1*100),2),np.round((accuracy_1*100),2)]),ignore_index=True)

loss_2, accuracy_2 = model_2.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2",np.round((loss_2*100),2),np.round((accuracy_2*100),2)]),ignore_index=True)

loss_2a, accuracy_2a = model_2a.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_2a",np.round((loss_2a*100),2),np.round((accuracy_2a*100),2)]),ignore_index=True)

loss_3, accuracy_3 = model_3.evaluate(X_test,y_test)
RESULTS = RESULTS.append(pd.Series(["model_3",np.round((loss_3*100),2),np.round((accuracy_3*100),2)]),ignore_index=True)

RESULTS.columns = ['MODEL_NAME','LOSS','ACCURACY']

RESULTS.style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()

38/38 [==============================] - 19s 481ms/step - loss: 280.7041 - accuracy: 0.5000
38/38 [==============================] - 8s 213ms/step - loss: 1.3595 - accuracy: 0.5075
38/38 [==============================] - 28s 716ms/step - loss: 7709.5459 - accuracy: 0.5000
38/38 [==============================] - 21s 540ms/step - loss: 0.6297 - accuracy: 0.7083


RESULTS.to_csv("RMSProp_Optimizer.csv",index = False)


Train_jpg, Test_jpg = train_test_split(NAMES_DF_FINAL, test_size=0.2, random_state=50,stratify=NAMES_DF_FINAL['Image_Target'])


Train_jpg1, Val_jpg = train_test_split(Train_jpg, test_size=0.2, random_state=50,stratify=Train_jpg['Image_Target'])


print('Shape of Training Images:',Train_jpg1.shape)
print("")
print('Shape of Testing Images:',Test_jpg.shape)
print("")
print('Shape of Validation Images:',Val_jpg.shape)

Shape of Training Images: (3840, 6)

Shape of Testing Images: (1200, 6)

Shape of Validation Images: (960, 6)


dst1 = "Training_Images"
dst2 = "Validation_Images"
dst3 = "Testing_Images"

mode = 0o777

os.mkdir(dst1, mode)
os.mkdir(dst2, mode)
os.mkdir(dst3, mode)


# Source path
source = "Training_jpg"
 
# Destination path
destination = "Training_Images"

for item in Train_jpg1['NAMES_JPG']:
    file = item
    src_file = os.path.join(source+"/"+file)
    dst_file = os.path.join(destination+"/"+file)
    #print("src_file",src_file)
    #print("dst_file",dst_file)
    shutil.copyfile(src_file, dst_file)


# Source path
source = "Training_jpg"
 
# Destination path
destination = "Validation_Images"

for item in Val_jpg['NAMES_JPG']:
    file = item
    src_file = os.path.join(source+"/"+file)
    dst_file = os.path.join(destination+"/"+file)
    #print("src_file",src_file)
    #print("dst_file",dst_file)
    shutil.copyfile(src_file, dst_file)


# Source path
source = "Training_jpg"
 
# Destination path
destination = "Testing_Images"

for item in Test_jpg['NAMES_JPG']:
    file = item
    src_file = os.path.join(source+"/"+file)
    dst_file = os.path.join(destination+"/"+file)
    #print("src_file",src_file)
    #print("dst_file",dst_file)
    shutil.copyfile(src_file, dst_file)


Train_images_count = os.listdir("Training_Images") # your directory path
Training_number_files = len(Train_images_count)
print('Number of images in Training Folder:',Training_number_files)

Validation_images_count = os.listdir("Validation_Images") # your directory path
Validation_number_files = len(Validation_images_count)
print('\nNumber of images in Validation Folder:',Validation_number_files)

Test_images_count = os.listdir("Testing_Images") # your directory path
Test_number_files = len(Test_images_count)
print('\nNumber of images in Test Folder:',Test_number_files)

Number of images in Training Folder: 3840

Number of images in Validation Folder: 960

Number of images in Test Folder: 1200


Train_Target_category = tf.keras.utils.to_categorical(Train_jpg1["Image_Target"], num_classes=2)
Val_Target_category = tf.keras.utils.to_categorical(Val_jpg["Image_Target"], num_classes=2)
Test_Target_category = tf.keras.utils.to_categorical(Test_jpg["Image_Target"], num_classes=2)


Train_TC = np.asarray(Train_jpg1["Image_Target"]).astype('float32').reshape((-1,1))
Val_TC = np.asarray(Val_jpg["Image_Target"]).astype('float32').reshape((-1,1))
Test_TC = np.asarray(Test_jpg["Image_Target"]).astype('float32').reshape((-1,1))


Train_jpg1["Target_Cat"] = Train_jpg1['Image_Target'].apply(lambda x: "No" if x == 0 else "Yes")

Val_jpg["Target_Cat"] = Val_jpg['Image_Target'].apply(lambda x: "No" if x == 0 else "Yes")

Test_jpg["Target_Cat"] = Test_jpg['Image_Target'].apply(lambda x: "No" if x == 0 else "Yes")


Train_jpg1.sample(2)


Train_jpg1.sample(3)


train_datagen = ImageDataGenerator(preprocessing_function = preprocess_input, 
                                   rotation_range = 20, 
                                   width_shift_range = 0.2, 
                                   height_shift_range = 0.2, 
                                   shear_range=0.2, 
                                   zoom_range=0.2)

'''
train_datagen = ImageDataGenerator(preprocessing_function = preprocess_input,
                                   rotation_range = 20,
                                   width_shift_range = 0.2,
                                   height_shift_range = 0.2,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True,
                                   fill_mode = 'nearest')
'''

"\ntrain_datagen = ImageDataGenerator(preprocessing_function = preprocess_input,\n                                   rotation_range = 20,\n                                   width_shift_range = 0.2,\n                                   height_shift_range = 0.2,\n                                   shear_range = 0.2,\n                                   zoom_range = 0.2,\n                                   horizontal_flip = True,\n                                   fill_mode = 'nearest')\n"


train_generator = train_datagen.flow_from_dataframe(
    dataframe = Train_jpg1, 
    directory = "Training_Images", 
    x_col = "NAMES_JPG", 
    y_col = "Target_Cat",
    class_mode = "categorical", 
    target_size = (224,224), 
    batch_size = 32, # batch_size,
    shuffle = True)

Found 3840 validated image filenames belonging to 2 classes.


valid_datagen = ImageDataGenerator(preprocessing_function = preprocess_input)


valid_generator = valid_datagen.flow_from_dataframe(
    dataframe=Val_jpg, 
    directory="Validation_Images",
    x_col = "NAMES_JPG", 
    y_col="Target_Cat",
    class_mode="categorical", 
    target_size=(224,224),
    batch_size=32, # batch_size,
    shuffle = True)

Found 960 validated image filenames belonging to 2 classes.


Test_datagen = ImageDataGenerator(preprocessing_function = preprocess_input)


Test_generator = Test_datagen.flow_from_dataframe(
    dataframe=Test_jpg, 
    directory="Testing_Images",
    x_col = "NAMES_JPG", 
    y_col="Target_Cat",
    class_mode="categorical", 
    target_size=(224,224),
    batch_size=32, # batch_size,
    shuffle = True)

Found 1200 validated image filenames belonging to 2 classes.


def round_up(n, decimals=0): 
    multiplier = 10 ** decimals 
    return math.ceil(n * multiplier) / multiplier

STEP_SIZE_TRAIN=round_up(train_generator.n/train_generator.batch_size)
STEP_SIZE_VALID=round_up(valid_generator.n/valid_generator.batch_size)


optimizer = Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
model.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"]) 
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 224, 224, 32)      896       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 112, 112, 32)     0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 112, 112, 32)     128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 112, 112, 64)      18496     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 56, 56, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 56, 56, 64)       256       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 56, 56, 64)        0         
                                                                 
 flatten (Flatten)           (None, 200704)            0         
                                                                 
 dense (Dense)               (None, 128)               25690240  
                                                                 
 dense_1 (Dense)             (None, 64)                8256      
                                                                 
 dense_2 (Dense)             (None, 2)                 130       
                                                                 
=================================================================
Total params: 25,718,402
Trainable params: 25,718,210
Non-trainable params: 192
_________________________________________________________________


epochs = 10
history = model.fit_generator(
    generator=train_generator,
    steps_per_epoch=STEP_SIZE_TRAIN,
    validation_data=valid_generator,
    validation_steps=STEP_SIZE_VALID,
    epochs=epochs)

Epoch 1/10
120/120 [==============================] - 376s 3s/step - loss: 430164.6250 - accuracy: 0.5417 - val_loss: 385582.2812 - val_accuracy: 0.6365
Epoch 2/10
120/120 [==============================] - 343s 3s/step - loss: 416412.6875 - accuracy: 0.6021 - val_loss: 376150.7188 - val_accuracy: 0.6521
Epoch 3/10
120/120 [==============================] - 342s 3s/step - loss: 402261.3438 - accuracy: 0.5992 - val_loss: 365302.6250 - val_accuracy: 0.6187
Epoch 4/10
120/120 [==============================] - 341s 3s/step - loss: 392648.4688 - accuracy: 0.6021 - val_loss: 354030.9062 - val_accuracy: 0.6250
Epoch 5/10
120/120 [==============================] - 341s 3s/step - loss: 375942.5625 - accuracy: 0.6128 - val_loss: 342611.8125 - val_accuracy: 0.6323
Epoch 6/10
120/120 [==============================] - 342s 3s/step - loss: 365785.9688 - accuracy: 0.6096 - val_loss: 331324.6562 - val_accuracy: 0.6240
Epoch 7/10
120/120 [==============================] - 341s 3s/step - loss: 354067.9375 - accuracy: 0.6154 - val_loss: 319875.2812 - val_accuracy: 0.5958
Epoch 8/10
120/120 [==============================] - 341s 3s/step - loss: 340272.1250 - accuracy: 0.6359 - val_loss: 308486.1562 - val_accuracy: 0.6354
Epoch 9/10
120/120 [==============================] - 341s 3s/step - loss: 327947.3125 - accuracy: 0.6258 - val_loss: 297119.1250 - val_accuracy: 0.6042
Epoch 10/10
120/120 [==============================] - 340s 3s/step - loss: 320065.5625 - accuracy: 0.6339 - val_loss: 285674.8438 - val_accuracy: 0.6417


optimizer = Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
model_2.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"]) 
model_2.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 112, 112, 32)      4736      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 56, 56, 32)       0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 56, 56, 32)       128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 56, 56, 64)        51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 28, 28, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 28, 28, 64)       256       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 28, 28, 64)        0         
                                                                 
 conv2d_2 (Conv2D)           (None, 28, 28, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 14, 14, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 14, 14, 128)      512       
 hNormalization)                                                 
                                                                 
 flatten (Flatten)           (None, 25088)             0         
                                                                 
 dense (Dense)               (None, 128)               3211392   
                                                                 
 dense_1 (Dense)             (None, 84)                10836     
                                                                 
 dense_2 (Dense)             (None, 42)                3570      
                                                                 
 dense_3 (Dense)             (None, 2)                 86        
                                                                 
=================================================================
Total params: 3,356,636
Trainable params: 3,356,188
Non-trainable params: 448
_________________________________________________________________


epochs = 10
history_2 = model_2.fit_generator(
    generator=train_generator,
    steps_per_epoch=STEP_SIZE_TRAIN,
    validation_data=valid_generator,
    validation_steps=STEP_SIZE_VALID,
    epochs=epochs)

Epoch 1/10
120/120 [==============================] - 263s 2s/step - loss: 216717.4844 - accuracy: 0.5388 - val_loss: 190052.9375 - val_accuracy: 0.5979
Epoch 2/10
120/120 [==============================] - 262s 2s/step - loss: 202214.7188 - accuracy: 0.5151 - val_loss: 175923.6406 - val_accuracy: 0.5000
Epoch 3/10
120/120 [==============================] - 261s 2s/step - loss: 185344.5156 - accuracy: 0.5229 - val_loss: 162092.9688 - val_accuracy: 0.5094
Epoch 4/10
120/120 [==============================] - 268s 2s/step - loss: 171409.0625 - accuracy: 0.5073 - val_loss: 148565.8438 - val_accuracy: 0.5010
Epoch 5/10
120/120 [==============================] - 268s 2s/step - loss: 155862.4219 - accuracy: 0.5023 - val_loss: 135087.5625 - val_accuracy: 0.5000
Epoch 6/10
120/120 [==============================] - 264s 2s/step - loss: 142000.7031 - accuracy: 0.5078 - val_loss: 121572.3828 - val_accuracy: 0.5000
Epoch 7/10
120/120 [==============================] - 256s 2s/step - loss: 125898.8359 - accuracy: 0.5057 - val_loss: 108194.5078 - val_accuracy: 0.5000
Epoch 8/10
120/120 [==============================] - 256s 2s/step - loss: 111337.4141 - accuracy: 0.5063 - val_loss: 94847.7188 - val_accuracy: 0.5000
Epoch 9/10
120/120 [==============================] - 257s 2s/step - loss: 96196.2812 - accuracy: 0.5023 - val_loss: 81598.0859 - val_accuracy: 0.5000
Epoch 10/10
120/120 [==============================] - 255s 2s/step - loss: 81925.5234 - accuracy: 0.5003 - val_loss: 68515.7734 - val_accuracy: 0.5000


optimizer = Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
model_2a.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"]) 
model_2a.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 220, 220, 32)      2432      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 110, 110, 32)     0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 110, 110, 32)     128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 106, 106, 64)      51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 53, 53, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 53, 53, 64)       256       
 hNormalization)                                                 
                                                                 
 conv2d_2 (Conv2D)           (None, 51, 51, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 25, 25, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 25, 25, 128)      512       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 25, 25, 128)       0         
                                                                 
 conv2d_3 (Conv2D)           (None, 23, 23, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 11, 11, 256)      0         
 2D)                                                             
                                                                 
 batch_normalization_3 (Batc  (None, 11, 11, 256)      1024      
 hNormalization)                                                 
                                                                 
 dropout_1 (Dropout)         (None, 11, 11, 256)       0         
                                                                 
 flatten (Flatten)           (None, 30976)             0         
                                                                 
 dense (Dense)               (None, 256)               7930112   
                                                                 
 dropout_2 (Dropout)         (None, 256)               0         
                                                                 
 dense_1 (Dense)             (None, 128)               32896     
                                                                 
 dense_2 (Dense)             (None, 64)                8256      
                                                                 
 dense_3 (Dense)             (None, 32)                2080      
                                                                 
 dense_4 (Dense)             (None, 2)                 66        
                                                                 
=================================================================
Total params: 8,398,050
Trainable params: 8,397,090
Non-trainable params: 960
_________________________________________________________________


epochs = 10
history_2a = model_2a.fit_generator(
    generator=train_generator,
    steps_per_epoch=STEP_SIZE_TRAIN,
    validation_data=valid_generator,
    validation_steps=STEP_SIZE_VALID,
    epochs=epochs)

Epoch 1/10
120/120 [==============================] - 749s 6s/step - loss: 436355.0000 - accuracy: 0.5714 - val_loss: 372988.6875 - val_accuracy: 0.5375
Epoch 2/10
120/120 [==============================] - 720s 6s/step - loss: 396310.9688 - accuracy: 0.5883 - val_loss: 344970.6250 - val_accuracy: 0.5000
Epoch 3/10
120/120 [==============================] - 3376s 28s/step - loss: 364014.9688 - accuracy: 0.5401 - val_loss: 316454.5625 - val_accuracy: 0.5000
Epoch 4/10
120/120 [==============================] - 714s 6s/step - loss: 339658.0000 - accuracy: 0.5451 - val_loss: 289452.6875 - val_accuracy: 0.4990
Epoch 5/10
120/120 [==============================] - 720s 6s/step - loss: 305067.5000 - accuracy: 0.5596 - val_loss: 263146.4688 - val_accuracy: 0.5625
Epoch 6/10
120/120 [==============================] - 5765s 48s/step - loss: 277047.8438 - accuracy: 0.5688 - val_loss: 237048.2656 - val_accuracy: 0.5146
Epoch 7/10
120/120 [==============================] - 588s 5s/step - loss: 249290.9531 - accuracy: 0.5802 - val_loss: 211155.3906 - val_accuracy: 0.5354
Epoch 8/10
120/120 [==============================] - 570s 5s/step - loss: 218804.9062 - accuracy: 0.5734 - val_loss: 186023.1562 - val_accuracy: 0.5740
Epoch 9/10
120/120 [==============================] - 576s 5s/step - loss: 192098.8281 - accuracy: 0.5698 - val_loss: 162182.0938 - val_accuracy: 0.6187
Epoch 10/10
120/120 [==============================] - 572s 5s/step - loss: 163746.9219 - accuracy: 0.5786 - val_loss: 140253.5312 - val_accuracy: 0.6333


optimizer = Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
model_3.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"]) 
model_3.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 224, 224, 32)      896       
                                                                 
 max_pooling2d (MaxPooling2D  (None, 112, 112, 32)     0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 112, 112, 32)     128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 112, 112, 64)      18496     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 56, 56, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 56, 56, 64)       256       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 56, 56, 64)        0         
                                                                 
 conv2d_2 (Conv2D)           (None, 56, 56, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 28, 28, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 28, 28, 128)      512       
 hNormalization)                                                 
                                                                 
 conv2d_3 (Conv2D)           (None, 28, 28, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 14, 14, 256)      0         
 2D)                                                             
                                                                 
 batch_normalization_3 (Batc  (None, 14, 14, 256)      1024      
 hNormalization)                                                 
                                                                 
 flatten (Flatten)           (None, 50176)             0         
                                                                 
 dense (Dense)               (None, 824)               41345848  
                                                                 
 dense_1 (Dense)             (None, 84)                69300     
                                                                 
 dense_2 (Dense)             (None, 42)                3570      
                                                                 
 dropout_1 (Dropout)         (None, 42)                0         
                                                                 
 dense_3 (Dense)             (None, 2)                 86        
                                                                 
=================================================================
Total params: 41,809,140
Trainable params: 41,808,180
Non-trainable params: 960
_________________________________________________________________


epochs = 10
history_3 = model_3.fit_generator(
    generator=train_generator,
    steps_per_epoch=STEP_SIZE_TRAIN,
    validation_data=valid_generator,
    validation_steps=STEP_SIZE_VALID,
    epochs=epochs)

Epoch 1/10
120/120 [==============================] - 464s 4s/step - loss: 0.8961 - accuracy: 0.5992 - val_loss: 0.6608 - val_accuracy: 0.5865
Epoch 2/10
120/120 [==============================] - 417s 3s/step - loss: 0.6789 - accuracy: 0.6359 - val_loss: 0.6316 - val_accuracy: 0.6646
Epoch 3/10
120/120 [==============================] - 441s 4s/step - loss: 0.6567 - accuracy: 0.6500 - val_loss: 0.6065 - val_accuracy: 0.6687
Epoch 4/10
120/120 [==============================] - 474s 4s/step - loss: 0.6378 - accuracy: 0.6703 - val_loss: 0.5803 - val_accuracy: 0.7063
Epoch 5/10
120/120 [==============================] - 591s 5s/step - loss: 0.6161 - accuracy: 0.6753 - val_loss: 0.5961 - val_accuracy: 0.6823
Epoch 6/10
120/120 [==============================] - 582s 5s/step - loss: 0.6287 - accuracy: 0.6661 - val_loss: 0.5706 - val_accuracy: 0.7125
Epoch 7/10
120/120 [==============================] - 577s 5s/step - loss: 0.6133 - accuracy: 0.6737 - val_loss: 0.5638 - val_accuracy: 0.7135
Epoch 8/10
120/120 [==============================] - 576s 5s/step - loss: 0.6160 - accuracy: 0.6763 - val_loss: 0.5827 - val_accuracy: 0.6833
Epoch 9/10
120/120 [==============================] - 423s 4s/step - loss: 0.6039 - accuracy: 0.6852 - val_loss: 0.5774 - val_accuracy: 0.6896
Epoch 10/10
120/120 [==============================] - 412s 3s/step - loss: 0.5992 - accuracy: 0.6909 - val_loss: 0.5844 - val_accuracy: 0.6927


RESULTS = pd.DataFrame()

loss_1, accuracy_1 = model.evaluate(Test_generator)
RESULTS = RESULTS.append(pd.Series(["model",np.round((loss_1*100),2),np.round((accuracy_1*100),2)]),ignore_index=True)

loss_2, accuracy_2 = model_2.evaluate(Test_generator)
RESULTS = RESULTS.append(pd.Series(["model_2",np.round((loss_2*100),2),np.round((accuracy_2*100),2)]),ignore_index=True)

loss_2a, accuracy_2a = model_2a.evaluate(Test_generator)
RESULTS = RESULTS.append(pd.Series(["model_2a",np.round((loss_2a*100),2),np.round((accuracy_2a*100),2)]),ignore_index=True)

loss_3, accuracy_3 = model_3.evaluate(Test_generator)
RESULTS = RESULTS.append(pd.Series(["model_3",np.round((loss_3*100),2),np.round((accuracy_3*100),2)]),ignore_index=True)

RESULTS.columns = ['MODEL_NAME','LOSS','ACCURACY']

RESULTS.style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()

38/38 [==============================] - 17s 434ms/step - loss: 295867.4688 - accuracy: 0.6333
38/38 [==============================] - 10s 273ms/step - loss: 14534.3945 - accuracy: 0.5000
38/38 [==============================] - 25s 639ms/step - loss: 145273.4219 - accuracy: 0.6142
38/38 [==============================] - 19s 486ms/step - loss: 0.6062 - accuracy: 0.6867


RESULTS.to_csv("Using_Image_data_generator.csv",index = False)


X_train10k, X_test10k, y_train10k, y_test10k = train_test_split(X_tar_10k, y1_tar_cat_10k, test_size=.30, 
                                                    stratify=y1_tar_cat_10k, random_state=1)  # 70% Training and 20% Testing


tf.keras.backend.clear_session()

# second model
model_2a = Sequential()

model_2a.add(Conv2D(filters=32, kernel_size=5, strides=(1, 1), activation="relu", input_shape=(224, 224, 3)))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Conv2D(filters=64, kernel_size=5, strides=(1, 1), activation="relu"))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Conv2D(filters=128, kernel_size=3, strides=(1, 1), activation="relu"))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Dropout(rate=0.2))

model_2a.add(Conv2D(filters=256, kernel_size=3, strides=(1, 1), activation="relu"))
model_2a.add(MaxPooling2D(pool_size=(2, 2)))
model_2a.add(BatchNormalization())

model_2a.add(Dropout(rate=0.2))

#model_2a.add(Conv2D(filters=512, kernel_size=3, strides=(1, 1), activation="relu"))
#model_2a.add(MaxPooling2D(pool_size=(2, 2)))
#model_2a.add(BatchNormalization())

model_2a.add(Flatten())

#model_2a.add(Dense(512, activation="relu"))
# model_2a.add(Dropout(rate=0.7))

model_2a.add(Dense(256, activation="relu"))
model_2a.add(Dropout(rate=0.25))

model_2a.add(Dense(128, activation="relu"))
# model_2a.add(Dropout(rate=0.3))

model_2a.add(Dense(64, activation="relu"))

model_2a.add(Dense(32, activation="relu"))

model_2a.add(Dense(2, activation="sigmoid"))


optimizer = Adam(learning_rate=0.01)
model_2a.compile(optimizer = optimizer , loss = "binary_crossentropy", metrics=["accuracy"])

model_2a.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 220, 220, 32)      2432      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 110, 110, 32)     0         
 )                                                               
                                                                 
 batch_normalization (BatchN  (None, 110, 110, 32)     128       
 ormalization)                                                   
                                                                 
 conv2d_1 (Conv2D)           (None, 106, 106, 64)      51264     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 53, 53, 64)       0         
 2D)                                                             
                                                                 
 batch_normalization_1 (Batc  (None, 53, 53, 64)       256       
 hNormalization)                                                 
                                                                 
 conv2d_2 (Conv2D)           (None, 51, 51, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 25, 25, 128)      0         
 2D)                                                             
                                                                 
 batch_normalization_2 (Batc  (None, 25, 25, 128)      512       
 hNormalization)                                                 
                                                                 
 dropout (Dropout)           (None, 25, 25, 128)       0         
                                                                 
 conv2d_3 (Conv2D)           (None, 23, 23, 256)       295168    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 11, 11, 256)      0         
 2D)                                                             
                                                                 
 batch_normalization_3 (Batc  (None, 11, 11, 256)      1024      
 hNormalization)                                                 
                                                                 
 dropout_1 (Dropout)         (None, 11, 11, 256)       0         
                                                                 
 flatten (Flatten)           (None, 30976)             0         
                                                                 
 dense (Dense)               (None, 256)               7930112   
                                                                 
 dropout_2 (Dropout)         (None, 256)               0         
                                                                 
 dense_1 (Dense)             (None, 128)               32896     
                                                                 
 dense_2 (Dense)             (None, 64)                8256      
                                                                 
 dense_3 (Dense)             (None, 32)                2080      
                                                                 
 dense_4 (Dense)             (None, 2)                 66        
                                                                 
=================================================================
Total params: 8,398,050
Trainable params: 8,397,090
Non-trainable params: 960
_________________________________________________________________


batch_size = 70
nb_epochs = 20

history_final = model_2a.fit(X_train10k, y_train10k,
                             batch_size=batch_size,
                             epochs=nb_epochs, 
                             initial_epoch=0,
                             callbacks=[EarlyStopping(monitor='loss', patience=2, min_delta=0.001)])

Epoch 1/20
100/100 [==============================] - 649s 7s/step - loss: 0.5757 - accuracy: 0.7131
Epoch 2/20
100/100 [==============================] - 338s 3s/step - loss: 0.5713 - accuracy: 0.7149
Epoch 3/20
100/100 [==============================] - 333s 3s/step - loss: 0.5633 - accuracy: 0.7191
Epoch 4/20
100/100 [==============================] - 3329s 34s/step - loss: 0.5385 - accuracy: 0.7367
Epoch 5/20
100/100 [==============================] - 339s 3s/step - loss: 0.5289 - accuracy: 0.7437
Epoch 6/20
100/100 [==============================] - 334s 3s/step - loss: 0.5240 - accuracy: 0.7459
Epoch 7/20
100/100 [==============================] - 332s 3s/step - loss: 0.5045 - accuracy: 0.7550
Epoch 8/20
100/100 [==============================] - 333s 3s/step - loss: 0.4956 - accuracy: 0.7616
Epoch 9/20
100/100 [==============================] - 332s 3s/step - loss: 0.4948 - accuracy: 0.7636
Epoch 10/20
100/100 [==============================] - 297s 3s/step - loss: 0.5021 - accuracy: 0.7606


model_2a.evaluate(X_test10k,y_test10k)

94/94 [==============================] - 19s 200ms/step - loss: 0.5642 - accuracy: 0.7340

[0.5642110109329224, 0.734000027179718]


y_pred_2a = model_2a.predict(X_test10k)
y_test_arg=np.argmax(y_test10k,axis=1)
Y_pred_2a = np.argmax(y_pred_2a,axis=1)
print(confusion_matrix(y_test_arg, Y_pred_2a))

94/94 [==============================] - 20s 204ms/step
[[ 985  515]
 [ 283 1217]]


print(classification_report(y_test_arg, Y_pred_2a))

              precision    recall  f1-score   support

           0       0.78      0.66      0.71      1500
           1       0.70      0.81      0.75      1500

    accuracy                           0.73      3000
   macro avg       0.74      0.73      0.73      3000
weighted avg       0.74      0.73      0.73      3000


cm = confusion_matrix(y_test_arg, Y_pred_2a)

labels = ["0 - No Pneumonia", "1 - Pneumonia"]

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)

disp.plot()

plt.show()


model_vgg = VGG16(input_shape=(224,224,3), weights='imagenet', include_top=False)
print(model_vgg.summary())

Model: "vgg16"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_1 (InputLayer)        [(None, 224, 224, 3)]     0         
                                                                 
 block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      
                                                                 
 block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     
                                                                 
 block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         
                                                                 
 block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     
                                                                 
 block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    
                                                                 
 block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         
                                                                 
 block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    
                                                                 
 block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         
                                                                 
 block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   
                                                                 
 block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         
                                                                 
 block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         
                                                                 
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
_________________________________________________________________
None


# don't train existing weights
for layer in model_vgg.layers:
    layer.trainable = False


# our layers
x = Flatten()(model_vgg.output)

# Add a fully connected layer with 512 hidden units and ReLU activation
x = Dense(128, kernel_initializer='he_normal', activation='relu')(x)

x = Dense(84, kernel_initializer='he_normal', activation='relu')(x)

x = Dense(42, kernel_initializer='he_normal', activation='relu')(x)

# Add a dropout rate of 0.5
x = Dropout(0.3)(x)

# Add a final sigmoid layer with 1 node for classification output
x = Dense(2, activation='sigmoid')(x)

model_vgg_1 = tf.keras.models.Model(model_vgg.input, x)

model_vgg_1.compile(optimizer = tf.keras.optimizers.RMSprop(lr=0.0001), loss = 'binary_crossentropy',metrics = ['acc'])
print(x)

# x = Dense(1000, activation='relu')(x)
# prediction = Dense(2, activation='sigmoid')(x)

KerasTensor(type_spec=TensorSpec(shape=(None, 2), dtype=tf.float32, name=None), name='dense_12/Sigmoid:0', description="created by layer 'dense_12'")


#Using pixel arrays

batch_size = 60
nb_epochs = 10

history_vggi = model_vgg_1.fit(X_train1,y_train1, 
                               batch_size=batch_size, 
                               epochs=nb_epochs,  
                               validation_data=(X_val,y_val), 
                               initial_epoch=0)

Epoch 1/10
64/64 [==============================] - 313s 5s/step - loss: 0.3477 - acc: 0.8586 - val_loss: 0.7265 - val_acc: 0.6865
Epoch 2/10
64/64 [==============================] - 339s 5s/step - loss: 0.2760 - acc: 0.8948 - val_loss: 0.8873 - val_acc: 0.7198
Epoch 3/10
64/64 [==============================] - 338s 5s/step - loss: 0.2010 - acc: 0.9253 - val_loss: 0.9280 - val_acc: 0.7208
Epoch 4/10
64/64 [==============================] - 338s 5s/step - loss: 0.1821 - acc: 0.9365 - val_loss: 0.9055 - val_acc: 0.7146
Epoch 5/10
64/64 [==============================] - 338s 5s/step - loss: 0.1453 - acc: 0.9471 - val_loss: 0.9951 - val_acc: 0.7156
Epoch 6/10
64/64 [==============================] - 338s 5s/step - loss: 0.1064 - acc: 0.9628 - val_loss: 1.1589 - val_acc: 0.7167
Epoch 7/10
64/64 [==============================] - 337s 5s/step - loss: 0.0915 - acc: 0.9703 - val_loss: 1.1338 - val_acc: 0.7198
Epoch 8/10
64/64 [==============================] - 340s 5s/step - loss: 0.0821 - acc: 0.9753 - val_loss: 1.4085 - val_acc: 0.7260
Epoch 9/10
64/64 [==============================] - 338s 5s/step - loss: 0.0756 - acc: 0.9745 - val_loss: 1.3455 - val_acc: 0.7115
Epoch 10/10
64/64 [==============================] - 338s 5s/step - loss: 0.0491 - acc: 0.9883 - val_loss: 1.6710 - val_acc: 0.7365


fig1 = plt.gcf()
plt.plot(history_vggi.history['acc'])
plt.plot(history_vggi.history['val_acc'])
plt.axis(ymin=0.4,ymax=1)
plt.grid()
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show()


fig1 = plt.gcf()
plt.plot(history_vggi.history['loss'])
plt.plot(history_vggi.history['val_loss'])
plt.axis(ymin=0.4,ymax=1)
plt.grid()
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show()


loss_trainvgg, accuracy_trainvgg = model_vgg_1.evaluate(X_train1,y_train1)
loss_valvgg, accuracy_valvgg = model_vgg_1.evaluate(X_val,y_val)
loss_testvgg, accuracy_testvgg = model_vgg_1.evaluate(X_test,y_test)

120/120 [==============================] - 214s 2s/step - loss: 0.0086 - acc: 0.9990
30/30 [==============================] - 68s 2s/step - loss: 1.6710 - acc: 0.7365
38/38 [==============================] - 70s 2s/step - loss: 1.6251 - acc: 0.7125


y_pred_vgg = model_vgg_1.predict(X_test)

38/38 [==============================] - 69s 2s/step


y_test_arg_vgg = np.argmax(y_test,axis=1)
y_pred_vgg = np.argmax(y_pred_vgg,axis=1)
print(confusion_matrix(y_test_arg_vgg, y_pred_vgg))

[[416 184]
 [161 439]]


Accuracy =metrics.accuracy_score(y_test_arg_vgg,y_pred_vgg)
Precision=metrics.precision_score(y_test_arg_vgg, y_pred_vgg)
Recall   =metrics.recall_score(y_test_arg_vgg, y_pred_vgg)
F1_score =metrics.f1_score(y_test_arg_vgg, y_pred_vgg)
ROC      =metrics.roc_auc_score(y_test_arg_vgg, y_pred_vgg)


Pre_trained_results = pd.DataFrame()

vgg_series = pd.Series(["VGG 16", Accuracy, Precision, Recall, F1_score, ROC])

Pre_trained_results = Pre_trained_results.append(vgg_series, ignore_index = True)


#Using Imagedatagenerator for train and val

STEP_SIZE_TRAIN=round_up(train_generator.n/train_generator.batch_size)
STEP_SIZE_VALID=round_up(valid_generator.n/valid_generator.batch_size)
                         
history_vggi = model_vgg_1.fit_generator(
    generator=train_generator,
    steps_per_epoch=STEP_SIZE_TRAIN,
    validation_data=valid_generator,
    validation_steps=STEP_SIZE_VALID,
    epochs=15)

Epoch 1/15
120/120 [==============================] - 311s 3s/step - loss: 1.4532 - acc: 0.6281 - val_loss: 0.6072 - val_acc: 0.7208
Epoch 2/15
120/120 [==============================] - 351s 3s/step - loss: 0.6461 - acc: 0.6867 - val_loss: 0.5692 - val_acc: 0.7469
Epoch 3/15
120/120 [==============================] - 350s 3s/step - loss: 0.6244 - acc: 0.6964 - val_loss: 0.5626 - val_acc: 0.7542
Epoch 4/15
120/120 [==============================] - 332s 3s/step - loss: 0.6046 - acc: 0.7146 - val_loss: 0.5542 - val_acc: 0.7469
Epoch 5/15
120/120 [==============================] - 340s 3s/step - loss: 0.5996 - acc: 0.7169 - val_loss: 0.5891 - val_acc: 0.7437
Epoch 6/15
120/120 [==============================] - 354s 3s/step - loss: 0.6023 - acc: 0.7359 - val_loss: 0.5317 - val_acc: 0.7615
Epoch 7/15
120/120 [==============================] - 294s 2s/step - loss: 0.5859 - acc: 0.7318 - val_loss: 0.5500 - val_acc: 0.7646
Epoch 8/15
120/120 [==============================] - 278s 2s/step - loss: 0.6011 - acc: 0.7302 - val_loss: 0.5882 - val_acc: 0.7708
Epoch 9/15
120/120 [==============================] - 278s 2s/step - loss: 0.5755 - acc: 0.7328 - val_loss: 0.5498 - val_acc: 0.7708
Epoch 10/15
120/120 [==============================] - 279s 2s/step - loss: 0.5627 - acc: 0.7258 - val_loss: 0.5462 - val_acc: 0.7677
Epoch 11/15
120/120 [==============================] - 278s 2s/step - loss: 0.5538 - acc: 0.7357 - val_loss: 0.6101 - val_acc: 0.7677
Epoch 12/15
120/120 [==============================] - 278s 2s/step - loss: 0.5675 - acc: 0.7437 - val_loss: 0.5402 - val_acc: 0.7615
Epoch 13/15
120/120 [==============================] - 277s 2s/step - loss: 0.5620 - acc: 0.7474 - val_loss: 0.5373 - val_acc: 0.7531
Epoch 14/15
120/120 [==============================] - 279s 2s/step - loss: 0.5668 - acc: 0.7411 - val_loss: 0.5501 - val_acc: 0.7677
Epoch 15/15
120/120 [==============================] - 277s 2s/step - loss: 0.5623 - acc: 0.7310 - val_loss: 0.5121 - val_acc: 0.7667


fig1 = plt.gcf()
plt.plot(history_vggi.history['acc'])
plt.plot(history_vggi.history['val_acc'])
plt.axis(ymin=0.4,ymax=1)
plt.grid()
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show()


fig1 = plt.gcf()
plt.plot(history_vggi.history['loss'])
plt.plot(history_vggi.history['val_loss'])
plt.axis(ymin=0.4,ymax=1)
plt.grid()
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show()


loss_trainvgg, accuracy_trainvgg = model_vgg_1.evaluate(train_generator)
loss_valvgg, accuracy_valvgg = model_vgg_1.evaluate(valid_generator)
loss_testvgg, accuracy_testvgg = model_vgg_1.evaluate(Test_generator)

120/120 [==============================] - 245s 2s/step - loss: 0.5128 - acc: 0.7646
30/30 [==============================] - 69s 2s/step - loss: 0.5121 - acc: 0.7667
38/38 [==============================] - 86s 2s/step - loss: 0.5654 - acc: 0.7425


y_pred_vgg = model_vgg_1.predict(X_test)

38/38 [==============================] - 70s 2s/step


y_test_arg_vgg = np.argmax(y_test,axis=1)
y_pred_vgg = np.argmax(y_pred_vgg,axis=1)
print(confusion_matrix(y_test_arg_vgg, y_pred_vgg))

[[495 105]
 [299 301]]


cm = confusion_matrix(y_test_arg_vgg, y_pred_vgg)

labels = ["0 - No Pneumonia", "1 - Pneumonia"]

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)

disp.plot()

plt.show()


Accuracy =metrics.accuracy_score(y_test_arg_vgg,y_pred_vgg)
Precision=metrics.precision_score(y_test_arg_vgg, y_pred_vgg)
Recall   =metrics.recall_score(y_test_arg_vgg, y_pred_vgg)
F1_score =metrics.f1_score(y_test_arg_vgg, y_pred_vgg)
ROC      =metrics.roc_auc_score(y_test_arg_vgg, y_pred_vgg)


vgg_series = pd.Series(["VGG 16 Using IDG", Accuracy, Precision, Recall, F1_score, ROC])

Pre_trained_results = Pre_trained_results.append(vgg_series, ignore_index = True)


print("VGG16 - Accuracy: ",Accuracy)
print("VGG16 - Precision: ",Precision)
print("VGG16 - Recall: ",Recall)
print("VGG16 - F1 score: ",F1_score)
print("VGG16 - ROC: ",ROC)

VGG16 - Accuracy:  0.6633333333333333
VGG16 - Precision:  0.7413793103448276
VGG16 - Recall:  0.5016666666666667
VGG16 - F1 score:  0.5984095427435389
VGG16 - ROC:  0.6633333333333333


resnet_model_1 = Sequential()

pretrained_model= tf.keras.applications.ResNet50(include_top=False, 
                                                 input_shape=(224,224,3), pooling='avg',classes=2, weights='imagenet')

for layer in pretrained_model.layers:
        layer.trainable=False

resnet_model_1.add(pretrained_model)


resnet_model_1.add(Flatten())
resnet_model_1.add(Dense(128, kernel_initializer='he_normal', activation='relu'))
resnet_model_1.add(Dense(64, kernel_initializer='he_normal', activation='relu'))
resnet_model_1.add(Dense(32, kernel_initializer='he_normal', activation='relu'))
resnet_model_1.add(Dense(2, kernel_initializer='he_normal', activation='sigmoid'))


resnet_model_1.summary()

Model: "sequential_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 resnet50 (Functional)       (None, 2048)              23587712  
                                                                 
 flatten_3 (Flatten)         (None, 2048)              0         
                                                                 
 dense_13 (Dense)            (None, 128)               262272    
                                                                 
 dense_14 (Dense)            (None, 64)                8256      
                                                                 
 dense_15 (Dense)            (None, 32)                2080      
                                                                 
 dense_16 (Dense)            (None, 2)                 66        
                                                                 
=================================================================
Total params: 23,860,386
Trainable params: 272,674
Non-trainable params: 23,587,712
_________________________________________________________________


resnet_model_1.compile(optimizer=Adam(learning_rate=0.00001),loss='binary_crossentropy',metrics=['acc'])


batch_size = 80
nb_epochs = 10
resnet_model_1_history = resnet_model_1.fit(X_train1, y_train1,
                                            batch_size=batch_size,
                                            epochs=nb_epochs, 
                                            validation_data=(X_val, y_val),
                                            initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
48/48 [==============================] - 187s 4s/step - loss: 0.7084 - acc: 0.5417 - val_loss: 0.6611 - val_acc: 0.6177
Epoch 2/10
48/48 [==============================] - 184s 4s/step - loss: 0.6440 - acc: 0.6461 - val_loss: 0.6257 - val_acc: 0.6906
Epoch 3/10
48/48 [==============================] - 193s 4s/step - loss: 0.6156 - acc: 0.6878 - val_loss: 0.6010 - val_acc: 0.6990
Epoch 4/10
48/48 [==============================] - 187s 4s/step - loss: 0.5956 - acc: 0.7068 - val_loss: 0.5845 - val_acc: 0.7000
Epoch 5/10
48/48 [==============================] - 187s 4s/step - loss: 0.5827 - acc: 0.7122 - val_loss: 0.5748 - val_acc: 0.7031
Epoch 6/10
48/48 [==============================] - 181s 4s/step - loss: 0.5742 - acc: 0.7211 - val_loss: 0.5684 - val_acc: 0.7052
Epoch 7/10
48/48 [==============================] - 182s 4s/step - loss: 0.5675 - acc: 0.7190 - val_loss: 0.5627 - val_acc: 0.7104
Epoch 8/10
48/48 [==============================] - 181s 4s/step - loss: 0.5627 - acc: 0.7214 - val_loss: 0.5584 - val_acc: 0.7125
Epoch 9/10
48/48 [==============================] - 181s 4s/step - loss: 0.5575 - acc: 0.7224 - val_loss: 0.5551 - val_acc: 0.7146
Epoch 10/10
48/48 [==============================] - 181s 4s/step - loss: 0.5537 - acc: 0.7260 - val_loss: 0.5520 - val_acc: 0.7188


fig1 = plt.gcf()
plt.plot(resnet_model_1_history.history['acc'])
plt.plot(resnet_model_1_history.history['val_acc'])
plt.axis(ymin=0.4,ymax=1)
plt.grid()
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show()


fig1 = plt.gcf()
plt.plot(resnet_model_1_history.history['loss'])
plt.plot(resnet_model_1_history.history['val_loss'])
plt.axis(ymin=0.4,ymax=1)
plt.grid()
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show()


loss_trainrn1, accuracy_trainrn1 = resnet_model_1.evaluate(X_train1,y_train1)
loss_valrn1, accuracy_valrn1 = resnet_model_1.evaluate(X_val,y_val)
loss_testrn1, accuracy_testrn1 = resnet_model_1.evaluate(X_test,y_test)

120/120 [==============================] - 147s 1s/step - loss: 0.5508 - acc: 0.7279
30/30 [==============================] - 37s 1s/step - loss: 0.5520 - acc: 0.7188
38/38 [==============================] - 46s 1s/step - loss: 0.5648 - acc: 0.7183


y_pred_rn1 = resnet_model_1.predict(X_test)

38/38 [==============================] - 47s 1s/step


y_test_arg_rn1 = np.argmax(y_test,axis=1)
y_pred_rn1 = np.argmax(y_pred_rn1,axis=1)
print(confusion_matrix(y_test_arg_rn1, y_pred_rn1))

[[416 184]
 [154 446]]


Accuracy =metrics.accuracy_score(y_test_arg_rn1,y_pred_rn1)
Precision=metrics.precision_score(y_test_arg_rn1, y_pred_rn1)
Recall   =metrics.recall_score(y_test_arg_rn1, y_pred_rn1)
F1_score =metrics.f1_score(y_test_arg_rn1, y_pred_rn1)
ROC      =metrics.roc_auc_score(y_test_arg_rn1, y_pred_rn1)


rn1_series = pd.Series(["RESTNET50", Accuracy, Precision, Recall, F1_score, ROC])

Pre_trained_results = Pre_trained_results.append(rn1_series, ignore_index = True)


print("RESTNET 50 - Accuracy: ",Accuracy)
print("RESTNET 50 - Precision: ",Precision)
print("RESTNET 50 - Recall: ",Recall)
print("RESTNET 50 - F1 score: ",F1_score)
print("RESTNET 50 - ROC: ",ROC)

RESTNET 50 - Accuracy:  0.7183333333333334
RESTNET 50 - Precision:  0.707936507936508
RESTNET 50 - Recall:  0.7433333333333333
RESTNET 50 - F1 score:  0.7252032520325203
RESTNET 50 - ROC:  0.7183333333333333


history_resneti = resnet_model_1.fit_generator(
    generator=train_generator,
    steps_per_epoch=STEP_SIZE_TRAIN,
    validation_data=valid_generator,
    validation_steps=STEP_SIZE_VALID,
    epochs=15)

Epoch 1/15
120/120 [==============================] - 241s 2s/step - loss: 0.6205 - acc: 0.6763 - val_loss: 0.5518 - val_acc: 0.7323
Epoch 2/15
120/120 [==============================] - 235s 2s/step - loss: 0.5708 - acc: 0.7273 - val_loss: 0.5348 - val_acc: 0.7406
Epoch 3/15
120/120 [==============================] - 232s 2s/step - loss: 0.5548 - acc: 0.7318 - val_loss: 0.5282 - val_acc: 0.7469
Epoch 4/15
120/120 [==============================] - 233s 2s/step - loss: 0.5492 - acc: 0.7349 - val_loss: 0.5253 - val_acc: 0.7500
Epoch 5/15
120/120 [==============================] - 232s 2s/step - loss: 0.5458 - acc: 0.7398 - val_loss: 0.5181 - val_acc: 0.7531
Epoch 6/15
120/120 [==============================] - 231s 2s/step - loss: 0.5341 - acc: 0.7451 - val_loss: 0.5192 - val_acc: 0.7500
Epoch 7/15
120/120 [==============================] - 232s 2s/step - loss: 0.5311 - acc: 0.7497 - val_loss: 0.5096 - val_acc: 0.7552
Epoch 8/15
120/120 [==============================] - 233s 2s/step - loss: 0.5341 - acc: 0.7422 - val_loss: 0.5099 - val_acc: 0.7521
Epoch 9/15
120/120 [==============================] - 233s 2s/step - loss: 0.5252 - acc: 0.7555 - val_loss: 0.5031 - val_acc: 0.7552
Epoch 10/15
120/120 [==============================] - 231s 2s/step - loss: 0.5281 - acc: 0.7445 - val_loss: 0.5113 - val_acc: 0.7542
Epoch 11/15
120/120 [==============================] - 231s 2s/step - loss: 0.5270 - acc: 0.7589 - val_loss: 0.5116 - val_acc: 0.7542
Epoch 12/15
120/120 [==============================] - 232s 2s/step - loss: 0.5171 - acc: 0.7557 - val_loss: 0.5100 - val_acc: 0.7583
Epoch 13/15
120/120 [==============================] - 232s 2s/step - loss: 0.5279 - acc: 0.7430 - val_loss: 0.5021 - val_acc: 0.7521
Epoch 14/15
120/120 [==============================] - 231s 2s/step - loss: 0.5188 - acc: 0.7482 - val_loss: 0.5129 - val_acc: 0.7594
Epoch 15/15
120/120 [==============================] - 231s 2s/step - loss: 0.5194 - acc: 0.7581 - val_loss: 0.5008 - val_acc: 0.7594


fig1 = plt.gcf()
plt.plot(history_resneti.history['acc'])
plt.plot(history_resneti.history['val_acc'])
plt.axis(ymin=0.4,ymax=1)
plt.grid()
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show()


fig1 = plt.gcf()
plt.plot(history_resneti.history['loss'])
plt.plot(history_resneti.history['val_loss'])
plt.axis(ymin=0.4,ymax=1)
plt.grid()
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show()


loss_trainrn2, accuracy_trainrn2 = resnet_model_1.evaluate(train_generator)
loss_valrn2, accuracy_valrn2 = resnet_model_1.evaluate(valid_generator)
loss_testrn2, accuracy_testrn2 = resnet_model_1.evaluate(Test_generator)

120/120 [==============================] - 176s 1s/step - loss: 0.5122 - acc: 0.7563
30/30 [==============================] - 43s 1s/step - loss: 0.5008 - acc: 0.7594
38/38 [==============================] - 53s 1s/step - loss: 0.5320 - acc: 0.7367


y_pred_rn2 = resnet_model_1.predict(X_test)

38/38 [==============================] - 51s 1s/step


y_test_arg_rn2 = np.argmax(y_test,axis=1)
y_pred_rn2 = np.argmax(y_pred_rn2,axis=1)
print(confusion_matrix(y_test_arg_rn2, y_pred_rn2))

[[441 159]
 [183 417]]


Accuracy =metrics.accuracy_score(y_test_arg_rn2,y_pred_rn2)
Precision=metrics.precision_score(y_test_arg_rn2, y_pred_rn2)
Recall   =metrics.recall_score(y_test_arg_rn2, y_pred_rn2)
F1_score =metrics.f1_score(y_test_arg_rn2, y_pred_rn2)
ROC      =metrics.roc_auc_score(y_test_arg_rn2, y_pred_rn2)


rn2_series = pd.Series(["RESTNET50_IMAGE DATA GENERATOR", Accuracy, Precision, Recall, F1_score, ROC])

Pre_trained_results = Pre_trained_results.append(rn2_series, ignore_index = True)


print("RESTNET 50 - Accuracy: ",Accuracy)
print("RESTNET 50 - Precision: ",Precision)
print("RESTNET 50 - Recall: ",Recall)
print("RESTNET 50 - F1 score: ",F1_score)
print("RESTNET 50 - ROC: ",ROC)

RESTNET 50 - Accuracy:  0.715
RESTNET 50 - Precision:  0.7239583333333334
RESTNET 50 - Recall:  0.695
RESTNET 50 - F1 score:  0.7091836734693878
RESTNET 50 - ROC:  0.715


incep_base_model = tf.keras.applications.InceptionV3(weights='imagenet', 
                                include_top=False, 
                                input_shape=(224, 224,3))
incep_base_model.trainable = False

add_model = Sequential()
add_model.add(incep_base_model)
add_model.add(GlobalAveragePooling2D())
add_model.add(Dropout(0.5))
add_model.add(Dense(2, 
                    activation='sigmoid'))

incep_model_1 = add_model
incep_model_1.compile(loss='binary_crossentropy', 
              optimizer=Adam(lr=1e-4),
              metrics=['accuracy'])
incep_model_1.summary()

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
87910968/87910968 [==============================] - 23s 0us/step
Model: "sequential_2"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 inception_v3 (Functional)   (None, 5, 5, 2048)        21802784  
                                                                 
 global_average_pooling2d (G  (None, 2048)             0         
 lobalAveragePooling2D)                                          
                                                                 
 dropout_5 (Dropout)         (None, 2048)              0         
                                                                 
 dense_17 (Dense)            (None, 2)                 4098      
                                                                 
=================================================================
Total params: 21,806,882
Trainable params: 4,098
Non-trainable params: 21,802,784
_________________________________________________________________


batch_size = 80
nb_epochs = 10
incep_hist_1 = incep_model_1.fit(X_train1, y_train1,
                                 batch_size=batch_size,
                                 epochs=nb_epochs, 
                                 validation_data=(X_val, y_val),
                                 initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
48/48 [==============================] - 95s 2s/step - loss: 6.3972 - accuracy: 0.5049 - val_loss: 1.6682 - val_accuracy: 0.5740
Epoch 2/10
48/48 [==============================] - 91s 2s/step - loss: 5.5562 - accuracy: 0.5440 - val_loss: 1.6705 - val_accuracy: 0.6125
Epoch 3/10
48/48 [==============================] - 92s 2s/step - loss: 5.2879 - accuracy: 0.5643 - val_loss: 1.7044 - val_accuracy: 0.6385


fig1 = plt.gcf()
plt.plot(incep_hist_1.history['accuracy'])
plt.plot(incep_hist_1.history['val_accuracy'])
plt.axis(ymin=0.4,ymax=1)
plt.grid()
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show()


loss_traininc1, accuracy_traininc1 = incep_model_1.evaluate(X_train1,y_train1)
loss_valinc1, accuracy_valinc1 = incep_model_1.evaluate(X_val,y_val)
loss_testinc1, accuracy_testinc1 = incep_model_1.evaluate(X_test,y_test)

120/120 [==============================] - 83s 683ms/step - loss: 1.6515 - accuracy: 0.6414
30/30 [==============================] - 22s 718ms/step - loss: 1.7044 - accuracy: 0.6385
38/38 [==============================] - 27s 702ms/step - loss: 1.7307 - accuracy: 0.6367


y_pred_inc1 = incep_model_1.predict(X_test)

38/38 [==============================] - 26s 664ms/step


y_test_arg_inc1 = np.argmax(y_test,axis=1)
y_pred_inc1 = np.argmax(y_pred_inc1,axis=1)
print(confusion_matrix(y_test_arg_inc1, y_pred_inc1))

[[414 186]
 [250 350]]


Accuracy =metrics.accuracy_score(y_test_arg_inc1,y_pred_inc1)
Precision=metrics.precision_score(y_test_arg_inc1, y_pred_inc1)
Recall   =metrics.recall_score(y_test_arg_inc1, y_pred_inc1)
F1_score =metrics.f1_score(y_test_arg_inc1, y_pred_inc1)
ROC      =metrics.roc_auc_score(y_test_arg_inc1, y_pred_inc1)


inc1_series = pd.Series(["INCEPTION V3", Accuracy, Precision, Recall, F1_score, ROC])

Pre_trained_results = Pre_trained_results.append(inc1_series, ignore_index = True)


print("INCEPTION V3 - Accuracy: ",Accuracy)
print("INCEPTION V3 - Precision: ",Precision)
print("INCEPTION V3 - Recall: ",Recall)
print("INCEPTION V3 - F1 score: ",F1_score)
print("INCEPTION V3 - ROC: ",ROC)

INCEPTION V3 - Accuracy:  0.6366666666666667
INCEPTION V3 - Precision:  0.6529850746268657
INCEPTION V3 - Recall:  0.5833333333333334
INCEPTION V3 - F1 score:  0.6161971830985915
INCEPTION V3 - ROC:  0.6366666666666667


history_inceptioni = incep_model_1.fit_generator(
    generator=train_generator,
    steps_per_epoch=STEP_SIZE_TRAIN,
    validation_data=valid_generator,
    validation_steps=STEP_SIZE_VALID,
    epochs=15)

Epoch 1/15
120/120 [==============================] - 144s 1s/step - loss: 8.4178 - accuracy: 0.5672 - val_loss: 2.4637 - val_accuracy: 0.6781
Epoch 2/15
120/120 [==============================] - 143s 1s/step - loss: 7.4747 - accuracy: 0.5745 - val_loss: 2.3133 - val_accuracy: 0.6865
Epoch 3/15
120/120 [==============================] - 142s 1s/step - loss: 6.7021 - accuracy: 0.5966 - val_loss: 2.2380 - val_accuracy: 0.6958
Epoch 4/15
120/120 [==============================] - 142s 1s/step - loss: 6.2577 - accuracy: 0.5969 - val_loss: 2.1274 - val_accuracy: 0.6958
Epoch 5/15
120/120 [==============================] - 142s 1s/step - loss: 5.9501 - accuracy: 0.5852 - val_loss: 1.9410 - val_accuracy: 0.7094
Epoch 6/15
120/120 [==============================] - 141s 1s/step - loss: 5.4407 - accuracy: 0.6000 - val_loss: 1.7987 - val_accuracy: 0.7104
Epoch 7/15
120/120 [==============================] - 142s 1s/step - loss: 4.9343 - accuracy: 0.5945 - val_loss: 1.7518 - val_accuracy: 0.7094
Epoch 8/15
120/120 [==============================] - 141s 1s/step - loss: 4.3727 - accuracy: 0.6094 - val_loss: 1.5628 - val_accuracy: 0.7115
Epoch 9/15
120/120 [==============================] - 142s 1s/step - loss: 4.1116 - accuracy: 0.6164 - val_loss: 1.4833 - val_accuracy: 0.7094
Epoch 10/15
120/120 [==============================] - 142s 1s/step - loss: 4.1056 - accuracy: 0.5940 - val_loss: 1.4073 - val_accuracy: 0.7135
Epoch 11/15
120/120 [==============================] - 142s 1s/step - loss: 3.5564 - accuracy: 0.6130 - val_loss: 1.3399 - val_accuracy: 0.7156
Epoch 12/15
120/120 [==============================] - 141s 1s/step - loss: 3.5717 - accuracy: 0.5932 - val_loss: 1.2253 - val_accuracy: 0.7156
Epoch 13/15
120/120 [==============================] - 142s 1s/step - loss: 3.2935 - accuracy: 0.6031 - val_loss: 1.1730 - val_accuracy: 0.7229
Epoch 14/15
120/120 [==============================] - 141s 1s/step - loss: 3.1212 - accuracy: 0.6018 - val_loss: 1.0627 - val_accuracy: 0.7177
Epoch 15/15
120/120 [==============================] - 142s 1s/step - loss: 2.7746 - accuracy: 0.6177 - val_loss: 1.0201 - val_accuracy: 0.7146


fig1 = plt.gcf()
plt.plot(history_inceptioni.history['accuracy'])
plt.plot(history_inceptioni.history['val_accuracy'])
plt.axis(ymin=0.4,ymax=1)
plt.grid()
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show()


loss_traininc2, accuracy_traininc2 = incep_model_1.evaluate(train_generator)
loss_valinc2, accuracy_valinc2 = incep_model_1.evaluate(valid_generator)
loss_testinc2, accuracy_testinc2 = incep_model_1.evaluate(Test_generator)

120/120 [==============================] - 118s 981ms/step - loss: 1.2315 - accuracy: 0.6802
30/30 [==============================] - 23s 755ms/step - loss: 1.0201 - accuracy: 0.7146
38/38 [==============================] - 28s 745ms/step - loss: 1.1289 - accuracy: 0.7083


y_pred_inc2 = incep_model_1.predict(X_test)

38/38 [==============================] - 25s 670ms/step


y_test_arg_inc2 = np.argmax(y_test,axis=1)
y_pred_inc2 = np.argmax(y_pred_inc2,axis=1)
print(confusion_matrix(y_test_arg_inc2, y_pred_inc2))

[[175 425]
 [ 72 528]]


Accuracy =metrics.accuracy_score(y_test_arg_inc2,y_pred_inc2)
Precision=metrics.precision_score(y_test_arg_inc2, y_pred_inc2)
Recall   =metrics.recall_score(y_test_arg_inc2, y_pred_inc2)
F1_score =metrics.f1_score(y_test_arg_inc2, y_pred_inc2)
ROC      =metrics.roc_auc_score(y_test_arg_inc2, y_pred_inc2)


inc2_series = pd.Series(["INCEPTION V3_IMAGE DATA GENERATOR", Accuracy, Precision, Recall, F1_score, ROC])

Pre_trained_results = Pre_trained_results.append(inc2_series, ignore_index = True)


print("INCEPTION V3 - Accuracy: ",Accuracy)
print("INCEPTION V3 - Precision: ",Precision)
print("INCEPTION V3 - Recall: ",Recall)
print("INCEPTION V3 - F1 score: ",F1_score)
print("INCEPTION V3 - ROC: ",ROC)

INCEPTION V3 - Accuracy:  0.5858333333333333
INCEPTION V3 - Precision:  0.5540398740818469
INCEPTION V3 - Recall:  0.88
INCEPTION V3 - F1 score:  0.6799742433998713
INCEPTION V3 - ROC:  0.5858333333333333


# Increase Trainable parameters in the model and retrain

add_model = Sequential()
add_model.add(incep_base_model)

add_model.add(GlobalAveragePooling2D())
# add_model.add(Dropout(0.3))
add_model.add(Dense(128, activation="relu"))
add_model.add(Dense(64, activation="relu"))
add_model.add(Dense(2, 
                    activation='sigmoid'))

incep_model_2 = add_model
incep_model_2.compile(loss='binary_crossentropy', 
              optimizer=RMSprop(lr=0.0001),
              metrics=['accuracy'])
incep_model_2.summary()

Model: "sequential_3"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 inception_v3 (Functional)   (None, 5, 5, 2048)        21802784  
                                                                 
 global_average_pooling2d_1   (None, 2048)             0         
 (GlobalAveragePooling2D)                                        
                                                                 
 dense_18 (Dense)            (None, 128)               262272    
                                                                 
 dense_19 (Dense)            (None, 64)                8256      
                                                                 
 dense_20 (Dense)            (None, 2)                 130       
                                                                 
=================================================================
Total params: 22,073,442
Trainable params: 270,658
Non-trainable params: 21,802,784
_________________________________________________________________


batch_size = 80
nb_epochs = 10
incep_hist_2 = incep_model_2.fit(X_train1, y_train1,
                                 batch_size=batch_size,
                                 epochs=nb_epochs, 
                                 validation_data=(X_val, y_val),
                                 initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/10
48/48 [==============================] - 95s 2s/step - loss: 1.1268 - accuracy: 0.6010 - val_loss: 0.9605 - val_accuracy: 0.6594
Epoch 2/10
48/48 [==============================] - 90s 2s/step - loss: 0.8434 - accuracy: 0.6430 - val_loss: 0.6872 - val_accuracy: 0.6719
Epoch 3/10
48/48 [==============================] - 88s 2s/step - loss: 0.7823 - accuracy: 0.6617 - val_loss: 0.7902 - val_accuracy: 0.6354
Epoch 4/10
48/48 [==============================] - 88s 2s/step - loss: 0.7374 - accuracy: 0.6682 - val_loss: 0.6929 - val_accuracy: 0.6875


fig1 = plt.gcf()
plt.plot(incep_hist_2.history['accuracy'])
plt.plot(incep_hist_2.history['val_accuracy'])
plt.axis(ymin=0.4,ymax=1)
plt.grid()
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show()


loss_traininc3, accuracy_traininc3 = incep_model_2.evaluate(train_generator)
loss_valinc3, accuracy_valinc3 = incep_model_2.evaluate(valid_generator)
loss_testinc3, accuracy_testinc3 = incep_model_2.evaluate(Test_generator)

120/120 [==============================] - 119s 980ms/step - loss: 1.8895 - accuracy: 0.5750
30/30 [==============================] - 22s 724ms/step - loss: 1.6969 - accuracy: 0.5948
38/38 [==============================] - 27s 718ms/step - loss: 1.7093 - accuracy: 0.5675


y_pred_inc3 = incep_model_2.predict(X_test)

38/38 [==============================] - 27s 695ms/step


y_test_arg_inc3 = np.argmax(y_test,axis=1)
y_pred_inc3 = np.argmax(y_pred_inc3,axis=1)
print(confusion_matrix(y_test_arg_inc3, y_pred_inc3))

[[412 188]
 [186 414]]


Accuracy =metrics.accuracy_score(y_test_arg_inc3,y_pred_inc3)
Precision=metrics.precision_score(y_test_arg_inc3, y_pred_inc3)
Recall   =metrics.recall_score(y_test_arg_inc3, y_pred_inc3)
F1_score =metrics.f1_score(y_test_arg_inc3, y_pred_inc3)
ROC      =metrics.roc_auc_score(y_test_arg_inc3, y_pred_inc3)


inc3_series = pd.Series(["INCEPTION V3 - ADDED LAYERS", Accuracy, Precision, Recall, F1_score, ROC])

Pre_trained_results = Pre_trained_results.append(inc3_series, ignore_index = True)


print("INCEPTION V3 - Accuracy: ",Accuracy)
print("INCEPTION V3 - Precision: ",Precision)
print("INCEPTION V3 - Recall: ",Recall)
print("INCEPTION V3 - F1 score: ",F1_score)
print("INCEPTION V3 - ROC: ",ROC)

INCEPTION V3 - Accuracy:  0.6883333333333334
INCEPTION V3 - Precision:  0.6877076411960132
INCEPTION V3 - Recall:  0.69
INCEPTION V3 - F1 score:  0.6888519134775374
INCEPTION V3 - ROC:  0.6883333333333332


cm = confusion_matrix(y_test_arg_inc3, y_pred_inc3)

labels = ["0 - No Pneumonia", "1 - Pneumonia"]

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)

disp.plot()

plt.show()


dense_base_model = tf.keras.applications.DenseNet121(
    include_top=False,
    weights="imagenet",
    input_shape=(224,224,3),
    pooling="avg"
    )


# don't train existing weights
for layer in dense_base_model.layers:
    layer.trainable = False


dense_base_model.summary()

Model: "densenet121"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_4 (InputLayer)           [(None, 224, 224, 3  0           []                               
                                )]                                                                
                                                                                                  
 zero_padding2d (ZeroPadding2D)  (None, 230, 230, 3)  0          ['input_4[0][0]']                
                                                                                                  
 conv1/conv (Conv2D)            (None, 112, 112, 64  9408        ['zero_padding2d[0][0]']         
                                )                                                                 
                                                                                                  
 conv1/bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1/conv[0][0]']             
                                )                                                                 
                                                                                                  
 conv1/relu (Activation)        (None, 112, 112, 64  0           ['conv1/bn[0][0]']               
                                )                                                                 
                                                                                                  
 zero_padding2d_1 (ZeroPadding2  (None, 114, 114, 64  0          ['conv1/relu[0][0]']             
 D)                             )                                                                 
                                                                                                  
 pool1 (MaxPooling2D)           (None, 56, 56, 64)   0           ['zero_padding2d_1[0][0]']       
                                                                                                  
 conv2_block1_0_bn (BatchNormal  (None, 56, 56, 64)  256         ['pool1[0][0]']                  
 ization)                                                                                         
                                                                                                  
 conv2_block1_0_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block1_1_conv (Conv2D)   (None, 56, 56, 128)  8192        ['conv2_block1_0_relu[0][0]']    
                                                                                                  
 conv2_block1_1_bn (BatchNormal  (None, 56, 56, 128)  512        ['conv2_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_1_relu (Activatio  (None, 56, 56, 128)  0          ['conv2_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block1_2_conv (Conv2D)   (None, 56, 56, 32)   36864       ['conv2_block1_1_relu[0][0]']    
                                                                                                  
 conv2_block1_concat (Concatena  (None, 56, 56, 96)  0           ['pool1[0][0]',                  
 te)                                                              'conv2_block1_2_conv[0][0]']    
                                                                                                  
 conv2_block2_0_bn (BatchNormal  (None, 56, 56, 96)  384         ['conv2_block1_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_0_relu (Activatio  (None, 56, 56, 96)  0           ['conv2_block2_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block2_1_conv (Conv2D)   (None, 56, 56, 128)  12288       ['conv2_block2_0_relu[0][0]']    
                                                                                                  
 conv2_block2_1_bn (BatchNormal  (None, 56, 56, 128)  512        ['conv2_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_1_relu (Activatio  (None, 56, 56, 128)  0          ['conv2_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block2_2_conv (Conv2D)   (None, 56, 56, 32)   36864       ['conv2_block2_1_relu[0][0]']    
                                                                                                  
 conv2_block2_concat (Concatena  (None, 56, 56, 128)  0          ['conv2_block1_concat[0][0]',    
 te)                                                              'conv2_block2_2_conv[0][0]']    
                                                                                                  
 conv2_block3_0_bn (BatchNormal  (None, 56, 56, 128)  512        ['conv2_block2_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_0_relu (Activatio  (None, 56, 56, 128)  0          ['conv2_block3_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block3_1_conv (Conv2D)   (None, 56, 56, 128)  16384       ['conv2_block3_0_relu[0][0]']    
                                                                                                  
 conv2_block3_1_bn (BatchNormal  (None, 56, 56, 128)  512        ['conv2_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_1_relu (Activatio  (None, 56, 56, 128)  0          ['conv2_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block3_2_conv (Conv2D)   (None, 56, 56, 32)   36864       ['conv2_block3_1_relu[0][0]']    
                                                                                                  
 conv2_block3_concat (Concatena  (None, 56, 56, 160)  0          ['conv2_block2_concat[0][0]',    
 te)                                                              'conv2_block3_2_conv[0][0]']    
                                                                                                  
 conv2_block4_0_bn (BatchNormal  (None, 56, 56, 160)  640        ['conv2_block3_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block4_0_relu (Activatio  (None, 56, 56, 160)  0          ['conv2_block4_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block4_1_conv (Conv2D)   (None, 56, 56, 128)  20480       ['conv2_block4_0_relu[0][0]']    
                                                                                                  
 conv2_block4_1_bn (BatchNormal  (None, 56, 56, 128)  512        ['conv2_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block4_1_relu (Activatio  (None, 56, 56, 128)  0          ['conv2_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block4_2_conv (Conv2D)   (None, 56, 56, 32)   36864       ['conv2_block4_1_relu[0][0]']    
                                                                                                  
 conv2_block4_concat (Concatena  (None, 56, 56, 192)  0          ['conv2_block3_concat[0][0]',    
 te)                                                              'conv2_block4_2_conv[0][0]']    
                                                                                                  
 conv2_block5_0_bn (BatchNormal  (None, 56, 56, 192)  768        ['conv2_block4_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block5_0_relu (Activatio  (None, 56, 56, 192)  0          ['conv2_block5_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block5_1_conv (Conv2D)   (None, 56, 56, 128)  24576       ['conv2_block5_0_relu[0][0]']    
                                                                                                  
 conv2_block5_1_bn (BatchNormal  (None, 56, 56, 128)  512        ['conv2_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block5_1_relu (Activatio  (None, 56, 56, 128)  0          ['conv2_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block5_2_conv (Conv2D)   (None, 56, 56, 32)   36864       ['conv2_block5_1_relu[0][0]']    
                                                                                                  
 conv2_block5_concat (Concatena  (None, 56, 56, 224)  0          ['conv2_block4_concat[0][0]',    
 te)                                                              'conv2_block5_2_conv[0][0]']    
                                                                                                  
 conv2_block6_0_bn (BatchNormal  (None, 56, 56, 224)  896        ['conv2_block5_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block6_0_relu (Activatio  (None, 56, 56, 224)  0          ['conv2_block6_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block6_1_conv (Conv2D)   (None, 56, 56, 128)  28672       ['conv2_block6_0_relu[0][0]']    
                                                                                                  
 conv2_block6_1_bn (BatchNormal  (None, 56, 56, 128)  512        ['conv2_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block6_1_relu (Activatio  (None, 56, 56, 128)  0          ['conv2_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block6_2_conv (Conv2D)   (None, 56, 56, 32)   36864       ['conv2_block6_1_relu[0][0]']    
                                                                                                  
 conv2_block6_concat (Concatena  (None, 56, 56, 256)  0          ['conv2_block5_concat[0][0]',    
 te)                                                              'conv2_block6_2_conv[0][0]']    
                                                                                                  
 pool2_bn (BatchNormalization)  (None, 56, 56, 256)  1024        ['conv2_block6_concat[0][0]']    
                                                                                                  
 pool2_relu (Activation)        (None, 56, 56, 256)  0           ['pool2_bn[0][0]']               
                                                                                                  
 pool2_conv (Conv2D)            (None, 56, 56, 128)  32768       ['pool2_relu[0][0]']             
                                                                                                  
 pool2_pool (AveragePooling2D)  (None, 28, 28, 128)  0           ['pool2_conv[0][0]']             
                                                                                                  
 conv3_block1_0_bn (BatchNormal  (None, 28, 28, 128)  512        ['pool2_pool[0][0]']             
 ization)                                                                                         
                                                                                                  
 conv3_block1_0_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  16384       ['conv3_block1_0_relu[0][0]']    
                                                                                                  
 conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block1_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block1_1_relu[0][0]']    
                                                                                                  
 conv3_block1_concat (Concatena  (None, 28, 28, 160)  0          ['pool2_pool[0][0]',             
 te)                                                              'conv3_block1_2_conv[0][0]']    
                                                                                                  
 conv3_block2_0_bn (BatchNormal  (None, 28, 28, 160)  640        ['conv3_block1_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_0_relu (Activatio  (None, 28, 28, 160)  0          ['conv3_block2_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  20480       ['conv3_block2_0_relu[0][0]']    
                                                                                                  
 conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block2_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block2_1_relu[0][0]']    
                                                                                                  
 conv3_block2_concat (Concatena  (None, 28, 28, 192)  0          ['conv3_block1_concat[0][0]',    
 te)                                                              'conv3_block2_2_conv[0][0]']    
                                                                                                  
 conv3_block3_0_bn (BatchNormal  (None, 28, 28, 192)  768        ['conv3_block2_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_0_relu (Activatio  (None, 28, 28, 192)  0          ['conv3_block3_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  24576       ['conv3_block3_0_relu[0][0]']    
                                                                                                  
 conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block3_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block3_1_relu[0][0]']    
                                                                                                  
 conv3_block3_concat (Concatena  (None, 28, 28, 224)  0          ['conv3_block2_concat[0][0]',    
 te)                                                              'conv3_block3_2_conv[0][0]']    
                                                                                                  
 conv3_block4_0_bn (BatchNormal  (None, 28, 28, 224)  896        ['conv3_block3_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_0_relu (Activatio  (None, 28, 28, 224)  0          ['conv3_block4_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  28672       ['conv3_block4_0_relu[0][0]']    
                                                                                                  
 conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block4_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block4_1_relu[0][0]']    
                                                                                                  
 conv3_block4_concat (Concatena  (None, 28, 28, 256)  0          ['conv3_block3_concat[0][0]',    
 te)                                                              'conv3_block4_2_conv[0][0]']    
                                                                                                  
 conv3_block5_0_bn (BatchNormal  (None, 28, 28, 256)  1024       ['conv3_block4_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block5_0_relu (Activatio  (None, 28, 28, 256)  0          ['conv3_block5_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block5_1_conv (Conv2D)   (None, 28, 28, 128)  32768       ['conv3_block5_0_relu[0][0]']    
                                                                                                  
 conv3_block5_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block5_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block5_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block5_1_relu[0][0]']    
                                                                                                  
 conv3_block5_concat (Concatena  (None, 28, 28, 288)  0          ['conv3_block4_concat[0][0]',    
 te)                                                              'conv3_block5_2_conv[0][0]']    
                                                                                                  
 conv3_block6_0_bn (BatchNormal  (None, 28, 28, 288)  1152       ['conv3_block5_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block6_0_relu (Activatio  (None, 28, 28, 288)  0          ['conv3_block6_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block6_1_conv (Conv2D)   (None, 28, 28, 128)  36864       ['conv3_block6_0_relu[0][0]']    
                                                                                                  
 conv3_block6_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block6_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block6_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block6_1_relu[0][0]']    
                                                                                                  
 conv3_block6_concat (Concatena  (None, 28, 28, 320)  0          ['conv3_block5_concat[0][0]',    
 te)                                                              'conv3_block6_2_conv[0][0]']    
                                                                                                  
 conv3_block7_0_bn (BatchNormal  (None, 28, 28, 320)  1280       ['conv3_block6_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block7_0_relu (Activatio  (None, 28, 28, 320)  0          ['conv3_block7_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block7_1_conv (Conv2D)   (None, 28, 28, 128)  40960       ['conv3_block7_0_relu[0][0]']    
                                                                                                  
 conv3_block7_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block7_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block7_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block7_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block7_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block7_1_relu[0][0]']    
                                                                                                  
 conv3_block7_concat (Concatena  (None, 28, 28, 352)  0          ['conv3_block6_concat[0][0]',    
 te)                                                              'conv3_block7_2_conv[0][0]']    
                                                                                                  
 conv3_block8_0_bn (BatchNormal  (None, 28, 28, 352)  1408       ['conv3_block7_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block8_0_relu (Activatio  (None, 28, 28, 352)  0          ['conv3_block8_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block8_1_conv (Conv2D)   (None, 28, 28, 128)  45056       ['conv3_block8_0_relu[0][0]']    
                                                                                                  
 conv3_block8_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block8_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block8_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block8_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block8_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block8_1_relu[0][0]']    
                                                                                                  
 conv3_block8_concat (Concatena  (None, 28, 28, 384)  0          ['conv3_block7_concat[0][0]',    
 te)                                                              'conv3_block8_2_conv[0][0]']    
                                                                                                  
 conv3_block9_0_bn (BatchNormal  (None, 28, 28, 384)  1536       ['conv3_block8_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block9_0_relu (Activatio  (None, 28, 28, 384)  0          ['conv3_block9_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block9_1_conv (Conv2D)   (None, 28, 28, 128)  49152       ['conv3_block9_0_relu[0][0]']    
                                                                                                  
 conv3_block9_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block9_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block9_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block9_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block9_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block9_1_relu[0][0]']    
                                                                                                  
 conv3_block9_concat (Concatena  (None, 28, 28, 416)  0          ['conv3_block8_concat[0][0]',    
 te)                                                              'conv3_block9_2_conv[0][0]']    
                                                                                                  
 conv3_block10_0_bn (BatchNorma  (None, 28, 28, 416)  1664       ['conv3_block9_concat[0][0]']    
 lization)                                                                                        
                                                                                                  
 conv3_block10_0_relu (Activati  (None, 28, 28, 416)  0          ['conv3_block10_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block10_1_conv (Conv2D)  (None, 28, 28, 128)  53248       ['conv3_block10_0_relu[0][0]']   
                                                                                                  
 conv3_block10_1_bn (BatchNorma  (None, 28, 28, 128)  512        ['conv3_block10_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block10_1_relu (Activati  (None, 28, 28, 128)  0          ['conv3_block10_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block10_2_conv (Conv2D)  (None, 28, 28, 32)   36864       ['conv3_block10_1_relu[0][0]']   
                                                                                                  
 conv3_block10_concat (Concaten  (None, 28, 28, 448)  0          ['conv3_block9_concat[0][0]',    
 ate)                                                             'conv3_block10_2_conv[0][0]']   
                                                                                                  
 conv3_block11_0_bn (BatchNorma  (None, 28, 28, 448)  1792       ['conv3_block10_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block11_0_relu (Activati  (None, 28, 28, 448)  0          ['conv3_block11_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block11_1_conv (Conv2D)  (None, 28, 28, 128)  57344       ['conv3_block11_0_relu[0][0]']   
                                                                                                  
 conv3_block11_1_bn (BatchNorma  (None, 28, 28, 128)  512        ['conv3_block11_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block11_1_relu (Activati  (None, 28, 28, 128)  0          ['conv3_block11_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block11_2_conv (Conv2D)  (None, 28, 28, 32)   36864       ['conv3_block11_1_relu[0][0]']   
                                                                                                  
 conv3_block11_concat (Concaten  (None, 28, 28, 480)  0          ['conv3_block10_concat[0][0]',   
 ate)                                                             'conv3_block11_2_conv[0][0]']   
                                                                                                  
 conv3_block12_0_bn (BatchNorma  (None, 28, 28, 480)  1920       ['conv3_block11_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block12_0_relu (Activati  (None, 28, 28, 480)  0          ['conv3_block12_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block12_1_conv (Conv2D)  (None, 28, 28, 128)  61440       ['conv3_block12_0_relu[0][0]']   
                                                                                                  
 conv3_block12_1_bn (BatchNorma  (None, 28, 28, 128)  512        ['conv3_block12_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block12_1_relu (Activati  (None, 28, 28, 128)  0          ['conv3_block12_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block12_2_conv (Conv2D)  (None, 28, 28, 32)   36864       ['conv3_block12_1_relu[0][0]']   
                                                                                                  
 conv3_block12_concat (Concaten  (None, 28, 28, 512)  0          ['conv3_block11_concat[0][0]',   
 ate)                                                             'conv3_block12_2_conv[0][0]']   
                                                                                                  
 pool3_bn (BatchNormalization)  (None, 28, 28, 512)  2048        ['conv3_block12_concat[0][0]']   
                                                                                                  
 pool3_relu (Activation)        (None, 28, 28, 512)  0           ['pool3_bn[0][0]']               
                                                                                                  
 pool3_conv (Conv2D)            (None, 28, 28, 256)  131072      ['pool3_relu[0][0]']             
                                                                                                  
 pool3_pool (AveragePooling2D)  (None, 14, 14, 256)  0           ['pool3_conv[0][0]']             
                                                                                                  
 conv4_block1_0_bn (BatchNormal  (None, 14, 14, 256)  1024       ['pool3_pool[0][0]']             
 ization)                                                                                         
                                                                                                  
 conv4_block1_0_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block1_1_conv (Conv2D)   (None, 14, 14, 128)  32768       ['conv4_block1_0_relu[0][0]']    
                                                                                                  
 conv4_block1_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block1_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block1_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block1_1_relu[0][0]']    
                                                                                                  
 conv4_block1_concat (Concatena  (None, 14, 14, 288)  0          ['pool3_pool[0][0]',             
 te)                                                              'conv4_block1_2_conv[0][0]']    
                                                                                                  
 conv4_block2_0_bn (BatchNormal  (None, 14, 14, 288)  1152       ['conv4_block1_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block2_0_relu (Activatio  (None, 14, 14, 288)  0          ['conv4_block2_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block2_1_conv (Conv2D)   (None, 14, 14, 128)  36864       ['conv4_block2_0_relu[0][0]']    
                                                                                                  
 conv4_block2_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block2_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block2_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block2_1_relu[0][0]']    
                                                                                                  
 conv4_block2_concat (Concatena  (None, 14, 14, 320)  0          ['conv4_block1_concat[0][0]',    
 te)                                                              'conv4_block2_2_conv[0][0]']    
                                                                                                  
 conv4_block3_0_bn (BatchNormal  (None, 14, 14, 320)  1280       ['conv4_block2_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block3_0_relu (Activatio  (None, 14, 14, 320)  0          ['conv4_block3_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block3_1_conv (Conv2D)   (None, 14, 14, 128)  40960       ['conv4_block3_0_relu[0][0]']    
                                                                                                  
 conv4_block3_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block3_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block3_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block3_1_relu[0][0]']    
                                                                                                  
 conv4_block3_concat (Concatena  (None, 14, 14, 352)  0          ['conv4_block2_concat[0][0]',    
 te)                                                              'conv4_block3_2_conv[0][0]']    
                                                                                                  
 conv4_block4_0_bn (BatchNormal  (None, 14, 14, 352)  1408       ['conv4_block3_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block4_0_relu (Activatio  (None, 14, 14, 352)  0          ['conv4_block4_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block4_1_conv (Conv2D)   (None, 14, 14, 128)  45056       ['conv4_block4_0_relu[0][0]']    
                                                                                                  
 conv4_block4_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block4_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block4_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block4_1_relu[0][0]']    
                                                                                                  
 conv4_block4_concat (Concatena  (None, 14, 14, 384)  0          ['conv4_block3_concat[0][0]',    
 te)                                                              'conv4_block4_2_conv[0][0]']    
                                                                                                  
 conv4_block5_0_bn (BatchNormal  (None, 14, 14, 384)  1536       ['conv4_block4_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block5_0_relu (Activatio  (None, 14, 14, 384)  0          ['conv4_block5_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block5_1_conv (Conv2D)   (None, 14, 14, 128)  49152       ['conv4_block5_0_relu[0][0]']    
                                                                                                  
 conv4_block5_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block5_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block5_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block5_1_relu[0][0]']    
                                                                                                  
 conv4_block5_concat (Concatena  (None, 14, 14, 416)  0          ['conv4_block4_concat[0][0]',    
 te)                                                              'conv4_block5_2_conv[0][0]']    
                                                                                                  
 conv4_block6_0_bn (BatchNormal  (None, 14, 14, 416)  1664       ['conv4_block5_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block6_0_relu (Activatio  (None, 14, 14, 416)  0          ['conv4_block6_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block6_1_conv (Conv2D)   (None, 14, 14, 128)  53248       ['conv4_block6_0_relu[0][0]']    
                                                                                                  
 conv4_block6_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block6_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block6_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block6_1_relu[0][0]']    
                                                                                                  
 conv4_block6_concat (Concatena  (None, 14, 14, 448)  0          ['conv4_block5_concat[0][0]',    
 te)                                                              'conv4_block6_2_conv[0][0]']    
                                                                                                  
 conv4_block7_0_bn (BatchNormal  (None, 14, 14, 448)  1792       ['conv4_block6_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block7_0_relu (Activatio  (None, 14, 14, 448)  0          ['conv4_block7_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block7_1_conv (Conv2D)   (None, 14, 14, 128)  57344       ['conv4_block7_0_relu[0][0]']    
                                                                                                  
 conv4_block7_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block7_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block7_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block7_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block7_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block7_1_relu[0][0]']    
                                                                                                  
 conv4_block7_concat (Concatena  (None, 14, 14, 480)  0          ['conv4_block6_concat[0][0]',    
 te)                                                              'conv4_block7_2_conv[0][0]']    
                                                                                                  
 conv4_block8_0_bn (BatchNormal  (None, 14, 14, 480)  1920       ['conv4_block7_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block8_0_relu (Activatio  (None, 14, 14, 480)  0          ['conv4_block8_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block8_1_conv (Conv2D)   (None, 14, 14, 128)  61440       ['conv4_block8_0_relu[0][0]']    
                                                                                                  
 conv4_block8_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block8_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block8_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block8_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block8_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block8_1_relu[0][0]']    
                                                                                                  
 conv4_block8_concat (Concatena  (None, 14, 14, 512)  0          ['conv4_block7_concat[0][0]',    
 te)                                                              'conv4_block8_2_conv[0][0]']    
                                                                                                  
 conv4_block9_0_bn (BatchNormal  (None, 14, 14, 512)  2048       ['conv4_block8_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block9_0_relu (Activatio  (None, 14, 14, 512)  0          ['conv4_block9_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block9_1_conv (Conv2D)   (None, 14, 14, 128)  65536       ['conv4_block9_0_relu[0][0]']    
                                                                                                  
 conv4_block9_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block9_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block9_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block9_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block9_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block9_1_relu[0][0]']    
                                                                                                  
 conv4_block9_concat (Concatena  (None, 14, 14, 544)  0          ['conv4_block8_concat[0][0]',    
 te)                                                              'conv4_block9_2_conv[0][0]']    
                                                                                                  
 conv4_block10_0_bn (BatchNorma  (None, 14, 14, 544)  2176       ['conv4_block9_concat[0][0]']    
 lization)                                                                                        
                                                                                                  
 conv4_block10_0_relu (Activati  (None, 14, 14, 544)  0          ['conv4_block10_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block10_1_conv (Conv2D)  (None, 14, 14, 128)  69632       ['conv4_block10_0_relu[0][0]']   
                                                                                                  
 conv4_block10_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block10_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block10_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block10_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block10_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block10_1_relu[0][0]']   
                                                                                                  
 conv4_block10_concat (Concaten  (None, 14, 14, 576)  0          ['conv4_block9_concat[0][0]',    
 ate)                                                             'conv4_block10_2_conv[0][0]']   
                                                                                                  
 conv4_block11_0_bn (BatchNorma  (None, 14, 14, 576)  2304       ['conv4_block10_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block11_0_relu (Activati  (None, 14, 14, 576)  0          ['conv4_block11_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block11_1_conv (Conv2D)  (None, 14, 14, 128)  73728       ['conv4_block11_0_relu[0][0]']   
                                                                                                  
 conv4_block11_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block11_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block11_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block11_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block11_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block11_1_relu[0][0]']   
                                                                                                  
 conv4_block11_concat (Concaten  (None, 14, 14, 608)  0          ['conv4_block10_concat[0][0]',   
 ate)                                                             'conv4_block11_2_conv[0][0]']   
                                                                                                  
 conv4_block12_0_bn (BatchNorma  (None, 14, 14, 608)  2432       ['conv4_block11_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block12_0_relu (Activati  (None, 14, 14, 608)  0          ['conv4_block12_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block12_1_conv (Conv2D)  (None, 14, 14, 128)  77824       ['conv4_block12_0_relu[0][0]']   
                                                                                                  
 conv4_block12_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block12_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block12_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block12_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block12_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block12_1_relu[0][0]']   
                                                                                                  
 conv4_block12_concat (Concaten  (None, 14, 14, 640)  0          ['conv4_block11_concat[0][0]',   
 ate)                                                             'conv4_block12_2_conv[0][0]']   
                                                                                                  
 conv4_block13_0_bn (BatchNorma  (None, 14, 14, 640)  2560       ['conv4_block12_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block13_0_relu (Activati  (None, 14, 14, 640)  0          ['conv4_block13_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block13_1_conv (Conv2D)  (None, 14, 14, 128)  81920       ['conv4_block13_0_relu[0][0]']   
                                                                                                  
 conv4_block13_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block13_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block13_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block13_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block13_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block13_1_relu[0][0]']   
                                                                                                  
 conv4_block13_concat (Concaten  (None, 14, 14, 672)  0          ['conv4_block12_concat[0][0]',   
 ate)                                                             'conv4_block13_2_conv[0][0]']   
                                                                                                  
 conv4_block14_0_bn (BatchNorma  (None, 14, 14, 672)  2688       ['conv4_block13_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block14_0_relu (Activati  (None, 14, 14, 672)  0          ['conv4_block14_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block14_1_conv (Conv2D)  (None, 14, 14, 128)  86016       ['conv4_block14_0_relu[0][0]']   
                                                                                                  
 conv4_block14_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block14_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block14_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block14_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block14_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block14_1_relu[0][0]']   
                                                                                                  
 conv4_block14_concat (Concaten  (None, 14, 14, 704)  0          ['conv4_block13_concat[0][0]',   
 ate)                                                             'conv4_block14_2_conv[0][0]']   
                                                                                                  
 conv4_block15_0_bn (BatchNorma  (None, 14, 14, 704)  2816       ['conv4_block14_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block15_0_relu (Activati  (None, 14, 14, 704)  0          ['conv4_block15_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block15_1_conv (Conv2D)  (None, 14, 14, 128)  90112       ['conv4_block15_0_relu[0][0]']   
                                                                                                  
 conv4_block15_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block15_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block15_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block15_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block15_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block15_1_relu[0][0]']   
                                                                                                  
 conv4_block15_concat (Concaten  (None, 14, 14, 736)  0          ['conv4_block14_concat[0][0]',   
 ate)                                                             'conv4_block15_2_conv[0][0]']   
                                                                                                  
 conv4_block16_0_bn (BatchNorma  (None, 14, 14, 736)  2944       ['conv4_block15_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block16_0_relu (Activati  (None, 14, 14, 736)  0          ['conv4_block16_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block16_1_conv (Conv2D)  (None, 14, 14, 128)  94208       ['conv4_block16_0_relu[0][0]']   
                                                                                                  
 conv4_block16_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block16_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block16_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block16_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block16_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block16_1_relu[0][0]']   
                                                                                                  
 conv4_block16_concat (Concaten  (None, 14, 14, 768)  0          ['conv4_block15_concat[0][0]',   
 ate)                                                             'conv4_block16_2_conv[0][0]']   
                                                                                                  
 conv4_block17_0_bn (BatchNorma  (None, 14, 14, 768)  3072       ['conv4_block16_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block17_0_relu (Activati  (None, 14, 14, 768)  0          ['conv4_block17_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block17_1_conv (Conv2D)  (None, 14, 14, 128)  98304       ['conv4_block17_0_relu[0][0]']   
                                                                                                  
 conv4_block17_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block17_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block17_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block17_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block17_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block17_1_relu[0][0]']   
                                                                                                  
 conv4_block17_concat (Concaten  (None, 14, 14, 800)  0          ['conv4_block16_concat[0][0]',   
 ate)                                                             'conv4_block17_2_conv[0][0]']   
                                                                                                  
 conv4_block18_0_bn (BatchNorma  (None, 14, 14, 800)  3200       ['conv4_block17_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block18_0_relu (Activati  (None, 14, 14, 800)  0          ['conv4_block18_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block18_1_conv (Conv2D)  (None, 14, 14, 128)  102400      ['conv4_block18_0_relu[0][0]']   
                                                                                                  
 conv4_block18_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block18_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block18_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block18_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block18_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block18_1_relu[0][0]']   
                                                                                                  
 conv4_block18_concat (Concaten  (None, 14, 14, 832)  0          ['conv4_block17_concat[0][0]',   
 ate)                                                             'conv4_block18_2_conv[0][0]']   
                                                                                                  
 conv4_block19_0_bn (BatchNorma  (None, 14, 14, 832)  3328       ['conv4_block18_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block19_0_relu (Activati  (None, 14, 14, 832)  0          ['conv4_block19_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block19_1_conv (Conv2D)  (None, 14, 14, 128)  106496      ['conv4_block19_0_relu[0][0]']   
                                                                                                  
 conv4_block19_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block19_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block19_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block19_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block19_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block19_1_relu[0][0]']   
                                                                                                  
 conv4_block19_concat (Concaten  (None, 14, 14, 864)  0          ['conv4_block18_concat[0][0]',   
 ate)                                                             'conv4_block19_2_conv[0][0]']   
                                                                                                  
 conv4_block20_0_bn (BatchNorma  (None, 14, 14, 864)  3456       ['conv4_block19_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block20_0_relu (Activati  (None, 14, 14, 864)  0          ['conv4_block20_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block20_1_conv (Conv2D)  (None, 14, 14, 128)  110592      ['conv4_block20_0_relu[0][0]']   
                                                                                                  
 conv4_block20_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block20_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block20_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block20_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block20_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block20_1_relu[0][0]']   
                                                                                                  
 conv4_block20_concat (Concaten  (None, 14, 14, 896)  0          ['conv4_block19_concat[0][0]',   
 ate)                                                             'conv4_block20_2_conv[0][0]']   
                                                                                                  
 conv4_block21_0_bn (BatchNorma  (None, 14, 14, 896)  3584       ['conv4_block20_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block21_0_relu (Activati  (None, 14, 14, 896)  0          ['conv4_block21_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block21_1_conv (Conv2D)  (None, 14, 14, 128)  114688      ['conv4_block21_0_relu[0][0]']   
                                                                                                  
 conv4_block21_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block21_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block21_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block21_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block21_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block21_1_relu[0][0]']   
                                                                                                  
 conv4_block21_concat (Concaten  (None, 14, 14, 928)  0          ['conv4_block20_concat[0][0]',   
 ate)                                                             'conv4_block21_2_conv[0][0]']   
                                                                                                  
 conv4_block22_0_bn (BatchNorma  (None, 14, 14, 928)  3712       ['conv4_block21_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block22_0_relu (Activati  (None, 14, 14, 928)  0          ['conv4_block22_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block22_1_conv (Conv2D)  (None, 14, 14, 128)  118784      ['conv4_block22_0_relu[0][0]']   
                                                                                                  
 conv4_block22_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block22_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block22_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block22_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block22_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block22_1_relu[0][0]']   
                                                                                                  
 conv4_block22_concat (Concaten  (None, 14, 14, 960)  0          ['conv4_block21_concat[0][0]',   
 ate)                                                             'conv4_block22_2_conv[0][0]']   
                                                                                                  
 conv4_block23_0_bn (BatchNorma  (None, 14, 14, 960)  3840       ['conv4_block22_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block23_0_relu (Activati  (None, 14, 14, 960)  0          ['conv4_block23_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block23_1_conv (Conv2D)  (None, 14, 14, 128)  122880      ['conv4_block23_0_relu[0][0]']   
                                                                                                  
 conv4_block23_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block23_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block23_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block23_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block23_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block23_1_relu[0][0]']   
                                                                                                  
 conv4_block23_concat (Concaten  (None, 14, 14, 992)  0          ['conv4_block22_concat[0][0]',   
 ate)                                                             'conv4_block23_2_conv[0][0]']   
                                                                                                  
 conv4_block24_0_bn (BatchNorma  (None, 14, 14, 992)  3968       ['conv4_block23_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block24_0_relu (Activati  (None, 14, 14, 992)  0          ['conv4_block24_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block24_1_conv (Conv2D)  (None, 14, 14, 128)  126976      ['conv4_block24_0_relu[0][0]']   
                                                                                                  
 conv4_block24_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block24_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block24_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block24_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block24_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block24_1_relu[0][0]']   
                                                                                                  
 conv4_block24_concat (Concaten  (None, 14, 14, 1024  0          ['conv4_block23_concat[0][0]',   
 ate)                           )                                 'conv4_block24_2_conv[0][0]']   
                                                                                                  
 pool4_bn (BatchNormalization)  (None, 14, 14, 1024  4096        ['conv4_block24_concat[0][0]']   
                                )                                                                 
                                                                                                  
 pool4_relu (Activation)        (None, 14, 14, 1024  0           ['pool4_bn[0][0]']               
                                )                                                                 
                                                                                                  
 pool4_conv (Conv2D)            (None, 14, 14, 512)  524288      ['pool4_relu[0][0]']             
                                                                                                  
 pool4_pool (AveragePooling2D)  (None, 7, 7, 512)    0           ['pool4_conv[0][0]']             
                                                                                                  
 conv5_block1_0_bn (BatchNormal  (None, 7, 7, 512)   2048        ['pool4_pool[0][0]']             
 ization)                                                                                         
                                                                                                  
 conv5_block1_0_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block1_1_conv (Conv2D)   (None, 7, 7, 128)    65536       ['conv5_block1_0_relu[0][0]']    
                                                                                                  
 conv5_block1_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block1_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block1_1_relu[0][0]']    
                                                                                                  
 conv5_block1_concat (Concatena  (None, 7, 7, 544)   0           ['pool4_pool[0][0]',             
 te)                                                              'conv5_block1_2_conv[0][0]']    
                                                                                                  
 conv5_block2_0_bn (BatchNormal  (None, 7, 7, 544)   2176        ['conv5_block1_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_0_relu (Activatio  (None, 7, 7, 544)   0           ['conv5_block2_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block2_1_conv (Conv2D)   (None, 7, 7, 128)    69632       ['conv5_block2_0_relu[0][0]']    
                                                                                                  
 conv5_block2_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block2_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block2_1_relu[0][0]']    
                                                                                                  
 conv5_block2_concat (Concatena  (None, 7, 7, 576)   0           ['conv5_block1_concat[0][0]',    
 te)                                                              'conv5_block2_2_conv[0][0]']    
                                                                                                  
 conv5_block3_0_bn (BatchNormal  (None, 7, 7, 576)   2304        ['conv5_block2_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_0_relu (Activatio  (None, 7, 7, 576)   0           ['conv5_block3_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block3_1_conv (Conv2D)   (None, 7, 7, 128)    73728       ['conv5_block3_0_relu[0][0]']    
                                                                                                  
 conv5_block3_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block3_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block3_1_relu[0][0]']    
                                                                                                  
 conv5_block3_concat (Concatena  (None, 7, 7, 608)   0           ['conv5_block2_concat[0][0]',    
 te)                                                              'conv5_block3_2_conv[0][0]']    
                                                                                                  
 conv5_block4_0_bn (BatchNormal  (None, 7, 7, 608)   2432        ['conv5_block3_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block4_0_relu (Activatio  (None, 7, 7, 608)   0           ['conv5_block4_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block4_1_conv (Conv2D)   (None, 7, 7, 128)    77824       ['conv5_block4_0_relu[0][0]']    
                                                                                                  
 conv5_block4_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block4_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block4_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block4_1_relu[0][0]']    
                                                                                                  
 conv5_block4_concat (Concatena  (None, 7, 7, 640)   0           ['conv5_block3_concat[0][0]',    
 te)                                                              'conv5_block4_2_conv[0][0]']    
                                                                                                  
 conv5_block5_0_bn (BatchNormal  (None, 7, 7, 640)   2560        ['conv5_block4_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block5_0_relu (Activatio  (None, 7, 7, 640)   0           ['conv5_block5_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block5_1_conv (Conv2D)   (None, 7, 7, 128)    81920       ['conv5_block5_0_relu[0][0]']    
                                                                                                  
 conv5_block5_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block5_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block5_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block5_1_relu[0][0]']    
                                                                                                  
 conv5_block5_concat (Concatena  (None, 7, 7, 672)   0           ['conv5_block4_concat[0][0]',    
 te)                                                              'conv5_block5_2_conv[0][0]']    
                                                                                                  
 conv5_block6_0_bn (BatchNormal  (None, 7, 7, 672)   2688        ['conv5_block5_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block6_0_relu (Activatio  (None, 7, 7, 672)   0           ['conv5_block6_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block6_1_conv (Conv2D)   (None, 7, 7, 128)    86016       ['conv5_block6_0_relu[0][0]']    
                                                                                                  
 conv5_block6_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block6_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block6_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block6_1_relu[0][0]']    
                                                                                                  
 conv5_block6_concat (Concatena  (None, 7, 7, 704)   0           ['conv5_block5_concat[0][0]',    
 te)                                                              'conv5_block6_2_conv[0][0]']    
                                                                                                  
 conv5_block7_0_bn (BatchNormal  (None, 7, 7, 704)   2816        ['conv5_block6_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block7_0_relu (Activatio  (None, 7, 7, 704)   0           ['conv5_block7_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block7_1_conv (Conv2D)   (None, 7, 7, 128)    90112       ['conv5_block7_0_relu[0][0]']    
                                                                                                  
 conv5_block7_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block7_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block7_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block7_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block7_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block7_1_relu[0][0]']    
                                                                                                  
 conv5_block7_concat (Concatena  (None, 7, 7, 736)   0           ['conv5_block6_concat[0][0]',    
 te)                                                              'conv5_block7_2_conv[0][0]']    
                                                                                                  
 conv5_block8_0_bn (BatchNormal  (None, 7, 7, 736)   2944        ['conv5_block7_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block8_0_relu (Activatio  (None, 7, 7, 736)   0           ['conv5_block8_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block8_1_conv (Conv2D)   (None, 7, 7, 128)    94208       ['conv5_block8_0_relu[0][0]']    
                                                                                                  
 conv5_block8_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block8_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block8_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block8_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block8_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block8_1_relu[0][0]']    
                                                                                                  
 conv5_block8_concat (Concatena  (None, 7, 7, 768)   0           ['conv5_block7_concat[0][0]',    
 te)                                                              'conv5_block8_2_conv[0][0]']    
                                                                                                  
 conv5_block9_0_bn (BatchNormal  (None, 7, 7, 768)   3072        ['conv5_block8_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block9_0_relu (Activatio  (None, 7, 7, 768)   0           ['conv5_block9_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block9_1_conv (Conv2D)   (None, 7, 7, 128)    98304       ['conv5_block9_0_relu[0][0]']    
                                                                                                  
 conv5_block9_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block9_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block9_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block9_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block9_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block9_1_relu[0][0]']    
                                                                                                  
 conv5_block9_concat (Concatena  (None, 7, 7, 800)   0           ['conv5_block8_concat[0][0]',    
 te)                                                              'conv5_block9_2_conv[0][0]']    
                                                                                                  
 conv5_block10_0_bn (BatchNorma  (None, 7, 7, 800)   3200        ['conv5_block9_concat[0][0]']    
 lization)                                                                                        
                                                                                                  
 conv5_block10_0_relu (Activati  (None, 7, 7, 800)   0           ['conv5_block10_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block10_1_conv (Conv2D)  (None, 7, 7, 128)    102400      ['conv5_block10_0_relu[0][0]']   
                                                                                                  
 conv5_block10_1_bn (BatchNorma  (None, 7, 7, 128)   512         ['conv5_block10_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block10_1_relu (Activati  (None, 7, 7, 128)   0           ['conv5_block10_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block10_2_conv (Conv2D)  (None, 7, 7, 32)     36864       ['conv5_block10_1_relu[0][0]']   
                                                                                                  
 conv5_block10_concat (Concaten  (None, 7, 7, 832)   0           ['conv5_block9_concat[0][0]',    
 ate)                                                             'conv5_block10_2_conv[0][0]']   
                                                                                                  
 conv5_block11_0_bn (BatchNorma  (None, 7, 7, 832)   3328        ['conv5_block10_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block11_0_relu (Activati  (None, 7, 7, 832)   0           ['conv5_block11_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block11_1_conv (Conv2D)  (None, 7, 7, 128)    106496      ['conv5_block11_0_relu[0][0]']   
                                                                                                  
 conv5_block11_1_bn (BatchNorma  (None, 7, 7, 128)   512         ['conv5_block11_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block11_1_relu (Activati  (None, 7, 7, 128)   0           ['conv5_block11_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block11_2_conv (Conv2D)  (None, 7, 7, 32)     36864       ['conv5_block11_1_relu[0][0]']   
                                                                                                  
 conv5_block11_concat (Concaten  (None, 7, 7, 864)   0           ['conv5_block10_concat[0][0]',   
 ate)                                                             'conv5_block11_2_conv[0][0]']   
                                                                                                  
 conv5_block12_0_bn (BatchNorma  (None, 7, 7, 864)   3456        ['conv5_block11_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block12_0_relu (Activati  (None, 7, 7, 864)   0           ['conv5_block12_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block12_1_conv (Conv2D)  (None, 7, 7, 128)    110592      ['conv5_block12_0_relu[0][0]']   
                                                                                                  
 conv5_block12_1_bn (BatchNorma  (None, 7, 7, 128)   512         ['conv5_block12_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block12_1_relu (Activati  (None, 7, 7, 128)   0           ['conv5_block12_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block12_2_conv (Conv2D)  (None, 7, 7, 32)     36864       ['conv5_block12_1_relu[0][0]']   
                                                                                                  
 conv5_block12_concat (Concaten  (None, 7, 7, 896)   0           ['conv5_block11_concat[0][0]',   
 ate)                                                             'conv5_block12_2_conv[0][0]']   
                                                                                                  
 conv5_block13_0_bn (BatchNorma  (None, 7, 7, 896)   3584        ['conv5_block12_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block13_0_relu (Activati  (None, 7, 7, 896)   0           ['conv5_block13_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block13_1_conv (Conv2D)  (None, 7, 7, 128)    114688      ['conv5_block13_0_relu[0][0]']   
                                                                                                  
 conv5_block13_1_bn (BatchNorma  (None, 7, 7, 128)   512         ['conv5_block13_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block13_1_relu (Activati  (None, 7, 7, 128)   0           ['conv5_block13_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block13_2_conv (Conv2D)  (None, 7, 7, 32)     36864       ['conv5_block13_1_relu[0][0]']   
                                                                                                  
 conv5_block13_concat (Concaten  (None, 7, 7, 928)   0           ['conv5_block12_concat[0][0]',   
 ate)                                                             'conv5_block13_2_conv[0][0]']   
                                                                                                  
 conv5_block14_0_bn (BatchNorma  (None, 7, 7, 928)   3712        ['conv5_block13_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block14_0_relu (Activati  (None, 7, 7, 928)   0           ['conv5_block14_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block14_1_conv (Conv2D)  (None, 7, 7, 128)    118784      ['conv5_block14_0_relu[0][0]']   
                                                                                                  
 conv5_block14_1_bn (BatchNorma  (None, 7, 7, 128)   512         ['conv5_block14_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block14_1_relu (Activati  (None, 7, 7, 128)   0           ['conv5_block14_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block14_2_conv (Conv2D)  (None, 7, 7, 32)     36864       ['conv5_block14_1_relu[0][0]']   
                                                                                                  
 conv5_block14_concat (Concaten  (None, 7, 7, 960)   0           ['conv5_block13_concat[0][0]',   
 ate)                                                             'conv5_block14_2_conv[0][0]']   
                                                                                                  
 conv5_block15_0_bn (BatchNorma  (None, 7, 7, 960)   3840        ['conv5_block14_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block15_0_relu (Activati  (None, 7, 7, 960)   0           ['conv5_block15_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block15_1_conv (Conv2D)  (None, 7, 7, 128)    122880      ['conv5_block15_0_relu[0][0]']   
                                                                                                  
 conv5_block15_1_bn (BatchNorma  (None, 7, 7, 128)   512         ['conv5_block15_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block15_1_relu (Activati  (None, 7, 7, 128)   0           ['conv5_block15_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block15_2_conv (Conv2D)  (None, 7, 7, 32)     36864       ['conv5_block15_1_relu[0][0]']   
                                                                                                  
 conv5_block15_concat (Concaten  (None, 7, 7, 992)   0           ['conv5_block14_concat[0][0]',   
 ate)                                                             'conv5_block15_2_conv[0][0]']   
                                                                                                  
 conv5_block16_0_bn (BatchNorma  (None, 7, 7, 992)   3968        ['conv5_block15_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block16_0_relu (Activati  (None, 7, 7, 992)   0           ['conv5_block16_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block16_1_conv (Conv2D)  (None, 7, 7, 128)    126976      ['conv5_block16_0_relu[0][0]']   
                                                                                                  
 conv5_block16_1_bn (BatchNorma  (None, 7, 7, 128)   512         ['conv5_block16_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block16_1_relu (Activati  (None, 7, 7, 128)   0           ['conv5_block16_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block16_2_conv (Conv2D)  (None, 7, 7, 32)     36864       ['conv5_block16_1_relu[0][0]']   
                                                                                                  
 conv5_block16_concat (Concaten  (None, 7, 7, 1024)  0           ['conv5_block15_concat[0][0]',   
 ate)                                                             'conv5_block16_2_conv[0][0]']   
                                                                                                  
 bn (BatchNormalization)        (None, 7, 7, 1024)   4096        ['conv5_block16_concat[0][0]']   
                                                                                                  
 relu (Activation)              (None, 7, 7, 1024)   0           ['bn[0][0]']                     
                                                                                                  
 avg_pool (GlobalAveragePooling  (None, 1024)        0           ['relu[0][0]']                   
 2D)                                                                                              
                                                                                                  
==================================================================================================
Total params: 7,037,504
Trainable params: 0
Non-trainable params: 7,037,504
__________________________________________________________________________________________________


tf.keras.backend.clear_session()

model_dense_1 = Sequential()

model_dense_1.add(dense_base_model)
model_dense_1.add(Flatten())
model_dense_1.add(BatchNormalization())
model_dense_1.add(Dense(1024, activation='relu'))
model_dense_1.add(Dropout(0.6))
model_dense_1.add(BatchNormalization())
# model_dense_1.add(Dense(128, activation='relu'))
# model_dense_1.add(Dropout(0.5))
# model_dense_1.add(BatchNormalization())
# model_dense_1.add(Dense(64, activation='relu'))
# model_dense_1.add(Dropout(0.3))
model_dense_1.add(Dense(2, activation='sigmoid'))

model_dense_1.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 densenet121 (Functional)    (None, 1024)              7037504   
                                                                 
 flatten (Flatten)           (None, 1024)              0         
                                                                 
 batch_normalization (BatchN  (None, 1024)             4096      
 ormalization)                                                   
                                                                 
 dense (Dense)               (None, 1024)              1049600   
                                                                 
 dropout (Dropout)           (None, 1024)              0         
                                                                 
 batch_normalization_1 (Batc  (None, 1024)             4096      
 hNormalization)                                                 
                                                                 
 dense_1 (Dense)             (None, 2)                 2050      
                                                                 
=================================================================
Total params: 8,097,346
Trainable params: 1,055,746
Non-trainable params: 7,041,600
_________________________________________________________________


optimizer = Adam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)

model_dense_1.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['acc'])


batch_size = 60
nb_epochs = 12
model_dense_hist_1 = model_dense_1.fit(X_train1, y_train1,
                                       batch_size=batch_size,
                                       epochs=nb_epochs, 
                                       validation_data=(X_val, y_val),
                                       initial_epoch=0, callbacks=[EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001)])

Epoch 1/12
64/64 [==============================] - 298s 5s/step - loss: 0.7282 - acc: 0.6596 - val_loss: 0.7735 - val_acc: 0.6104
Epoch 2/12
64/64 [==============================] - 322s 5s/step - loss: 0.6637 - acc: 0.7036 - val_loss: 0.5831 - val_acc: 0.7073
Epoch 3/12
64/64 [==============================] - 343s 5s/step - loss: 0.6419 - acc: 0.7193 - val_loss: 0.5654 - val_acc: 0.7271
Epoch 4/12
64/64 [==============================] - 384s 6s/step - loss: 0.6205 - acc: 0.7271 - val_loss: 0.5502 - val_acc: 0.7406
Epoch 5/12
64/64 [==============================] - 397s 6s/step - loss: 0.6024 - acc: 0.7318 - val_loss: 0.5477 - val_acc: 0.7406
Epoch 6/12
64/64 [==============================] - 311s 5s/step - loss: 0.5921 - acc: 0.7471 - val_loss: 0.5524 - val_acc: 0.7240
Epoch 7/12
64/64 [==============================] - 309s 5s/step - loss: 0.5812 - acc: 0.7346 - val_loss: 0.5460 - val_acc: 0.7281
Epoch 8/12
64/64 [==============================] - 311s 5s/step - loss: 0.5608 - acc: 0.7612 - val_loss: 0.5534 - val_acc: 0.7312
Epoch 9/12
64/64 [==============================] - 308s 5s/step - loss: 0.5663 - acc: 0.7492 - val_loss: 0.5455 - val_acc: 0.7240


fig1 = plt.gcf()
plt.plot(model_dense_hist_1.history['acc'])
plt.plot(model_dense_hist_1.history['val_acc'])
plt.axis(ymin=0.4,ymax=1)
plt.grid()
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show()


fig1 = plt.gcf()
plt.plot(model_dense_hist_1.history['loss'])
plt.plot(model_dense_hist_1.history['val_loss'])
plt.axis(ymin=0.4,ymax=1)
plt.grid()
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show()


loss_traindn1, accuracy_traindn1 = model_dense_1.evaluate(X_train1,y_train1)
loss_valdn1, accuracy_valdn1 = model_dense_1.evaluate(X_val,y_val)
loss_testdn1, accuracy_testdn1 = model_dense_1.evaluate(X_test,y_test)

120/120 [==============================] - 261s 2s/step - loss: 0.4536 - acc: 0.7833
30/30 [==============================] - 69s 2s/step - loss: 0.5455 - acc: 0.7240
38/38 [==============================] - 86s 2s/step - loss: 0.5565 - acc: 0.7308


y_pred_dn1 = model_dense_1.predict(X_test)

38/38 [==============================] - 81s 2s/step


y_test_arg_dn1 = np.argmax(y_test,axis=1)
y_pred_dn1 = np.argmax(y_pred_dn1,axis=1)
print(confusion_matrix(y_test_arg_dn1, y_pred_dn1))

[[434 166]
 [157 443]]


Accuracy =metrics.accuracy_score(y_test_arg_dn1,y_pred_dn1)
Precision=metrics.precision_score(y_test_arg_dn1, y_pred_dn1)
Recall   =metrics.recall_score(y_test_arg_dn1, y_pred_dn1)
F1_score =metrics.f1_score(y_test_arg_dn1, y_pred_dn1)
ROC      =metrics.roc_auc_score(y_test_arg_dn1, y_pred_dn1)


dn1_series = pd.Series(["DENSENET121", Accuracy, Precision, Recall, F1_score, ROC])

Pre_trained_results = Pre_trained_results.append(dn1_series, ignore_index = True)


print("DENSENET121 - Accuracy: ",Accuracy)
print("DENSENET121 - Precision: ",Precision)
print("DENSENET121 - Recall: ",Recall)
print("DENSENET121 - F1 score: ",F1_score)
print("DENSENET121 - ROC: ",ROC)

DENSENET121 - Accuracy:  0.7308333333333333
DENSENET121 - Precision:  0.7274220032840722
DENSENET121 - Recall:  0.7383333333333333
DENSENET121 - F1 score:  0.7328370554177005
DENSENET121 - ROC:  0.7308333333333333


cm = confusion_matrix(y_test_arg_dn1, y_pred_dn1)

labels = ["0 - No Pneumonia", "1 - Pneumonia"]

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)

disp.plot()

plt.show()


weights_path = "brucechou1983_CheXNet_Keras_0.3.0_weights.h5"


def build_model():
    base_model = tf.keras.applications.DenseNet121(weights=None,
                                                   include_top=False,
                                                   input_shape=(224,224,3), pooling="avg")
    # freezing initial layers of base model
    #for layer in base_model.layers:
       # layer.trainable = False

    base_model.load_weights(weights_path,by_name= True)

    x = tf.keras.layers.Dense(units = 256, activation = 'relu')(base_model.output)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.activations.relu(x)
    x = tf.keras.layers.Dropout(rate = 0.1)(x)
    x = tf.keras.layers.Dense(units = 128)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.activations.relu(x)
    predictions = tf.keras.layers.Dense(2, activation='softmax', name='predictions')(x)
    model = tf.keras.Model(inputs= base_model.input, outputs=predictions)

    learning_rate = 4.8637e-5
    #adamw = tfa.optimizers.AdamW(weight_decay= wd,learning_rate=learning_rate)
    optimizer = tf.keras.optimizers.Adam(learning_rate)
    model.compile(optimizer=optimizer,
    loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
    metrics=[tf.keras.metrics.AUC(),
    tf.keras.metrics.Precision(),
    tf.keras.metrics.Recall()])
    return model


new_dense_model = build_model()


new_dense_model.metrics

[]


new_dense_model.summary()

Model: "model"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               
                                )]                                                                
                                                                                                  
 zero_padding2d (ZeroPadding2D)  (None, 230, 230, 3)  0          ['input_1[0][0]']                
                                                                                                  
 conv1/conv (Conv2D)            (None, 112, 112, 64  9408        ['zero_padding2d[0][0]']         
                                )                                                                 
                                                                                                  
 conv1/bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1/conv[0][0]']             
                                )                                                                 
                                                                                                  
 conv1/relu (Activation)        (None, 112, 112, 64  0           ['conv1/bn[0][0]']               
                                )                                                                 
                                                                                                  
 zero_padding2d_1 (ZeroPadding2  (None, 114, 114, 64  0          ['conv1/relu[0][0]']             
 D)                             )                                                                 
                                                                                                  
 pool1 (MaxPooling2D)           (None, 56, 56, 64)   0           ['zero_padding2d_1[0][0]']       
                                                                                                  
 conv2_block1_0_bn (BatchNormal  (None, 56, 56, 64)  256         ['pool1[0][0]']                  
 ization)                                                                                         
                                                                                                  
 conv2_block1_0_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block1_1_conv (Conv2D)   (None, 56, 56, 128)  8192        ['conv2_block1_0_relu[0][0]']    
                                                                                                  
 conv2_block1_1_bn (BatchNormal  (None, 56, 56, 128)  512        ['conv2_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_1_relu (Activatio  (None, 56, 56, 128)  0          ['conv2_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block1_2_conv (Conv2D)   (None, 56, 56, 32)   36864       ['conv2_block1_1_relu[0][0]']    
                                                                                                  
 conv2_block1_concat (Concatena  (None, 56, 56, 96)  0           ['pool1[0][0]',                  
 te)                                                              'conv2_block1_2_conv[0][0]']    
                                                                                                  
 conv2_block2_0_bn (BatchNormal  (None, 56, 56, 96)  384         ['conv2_block1_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_0_relu (Activatio  (None, 56, 56, 96)  0           ['conv2_block2_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block2_1_conv (Conv2D)   (None, 56, 56, 128)  12288       ['conv2_block2_0_relu[0][0]']    
                                                                                                  
 conv2_block2_1_bn (BatchNormal  (None, 56, 56, 128)  512        ['conv2_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_1_relu (Activatio  (None, 56, 56, 128)  0          ['conv2_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block2_2_conv (Conv2D)   (None, 56, 56, 32)   36864       ['conv2_block2_1_relu[0][0]']    
                                                                                                  
 conv2_block2_concat (Concatena  (None, 56, 56, 128)  0          ['conv2_block1_concat[0][0]',    
 te)                                                              'conv2_block2_2_conv[0][0]']    
                                                                                                  
 conv2_block3_0_bn (BatchNormal  (None, 56, 56, 128)  512        ['conv2_block2_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_0_relu (Activatio  (None, 56, 56, 128)  0          ['conv2_block3_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block3_1_conv (Conv2D)   (None, 56, 56, 128)  16384       ['conv2_block3_0_relu[0][0]']    
                                                                                                  
 conv2_block3_1_bn (BatchNormal  (None, 56, 56, 128)  512        ['conv2_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_1_relu (Activatio  (None, 56, 56, 128)  0          ['conv2_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block3_2_conv (Conv2D)   (None, 56, 56, 32)   36864       ['conv2_block3_1_relu[0][0]']    
                                                                                                  
 conv2_block3_concat (Concatena  (None, 56, 56, 160)  0          ['conv2_block2_concat[0][0]',    
 te)                                                              'conv2_block3_2_conv[0][0]']    
                                                                                                  
 conv2_block4_0_bn (BatchNormal  (None, 56, 56, 160)  640        ['conv2_block3_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block4_0_relu (Activatio  (None, 56, 56, 160)  0          ['conv2_block4_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block4_1_conv (Conv2D)   (None, 56, 56, 128)  20480       ['conv2_block4_0_relu[0][0]']    
                                                                                                  
 conv2_block4_1_bn (BatchNormal  (None, 56, 56, 128)  512        ['conv2_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block4_1_relu (Activatio  (None, 56, 56, 128)  0          ['conv2_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block4_2_conv (Conv2D)   (None, 56, 56, 32)   36864       ['conv2_block4_1_relu[0][0]']    
                                                                                                  
 conv2_block4_concat (Concatena  (None, 56, 56, 192)  0          ['conv2_block3_concat[0][0]',    
 te)                                                              'conv2_block4_2_conv[0][0]']    
                                                                                                  
 conv2_block5_0_bn (BatchNormal  (None, 56, 56, 192)  768        ['conv2_block4_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block5_0_relu (Activatio  (None, 56, 56, 192)  0          ['conv2_block5_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block5_1_conv (Conv2D)   (None, 56, 56, 128)  24576       ['conv2_block5_0_relu[0][0]']    
                                                                                                  
 conv2_block5_1_bn (BatchNormal  (None, 56, 56, 128)  512        ['conv2_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block5_1_relu (Activatio  (None, 56, 56, 128)  0          ['conv2_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block5_2_conv (Conv2D)   (None, 56, 56, 32)   36864       ['conv2_block5_1_relu[0][0]']    
                                                                                                  
 conv2_block5_concat (Concatena  (None, 56, 56, 224)  0          ['conv2_block4_concat[0][0]',    
 te)                                                              'conv2_block5_2_conv[0][0]']    
                                                                                                  
 conv2_block6_0_bn (BatchNormal  (None, 56, 56, 224)  896        ['conv2_block5_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block6_0_relu (Activatio  (None, 56, 56, 224)  0          ['conv2_block6_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block6_1_conv (Conv2D)   (None, 56, 56, 128)  28672       ['conv2_block6_0_relu[0][0]']    
                                                                                                  
 conv2_block6_1_bn (BatchNormal  (None, 56, 56, 128)  512        ['conv2_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block6_1_relu (Activatio  (None, 56, 56, 128)  0          ['conv2_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block6_2_conv (Conv2D)   (None, 56, 56, 32)   36864       ['conv2_block6_1_relu[0][0]']    
                                                                                                  
 conv2_block6_concat (Concatena  (None, 56, 56, 256)  0          ['conv2_block5_concat[0][0]',    
 te)                                                              'conv2_block6_2_conv[0][0]']    
                                                                                                  
 pool2_bn (BatchNormalization)  (None, 56, 56, 256)  1024        ['conv2_block6_concat[0][0]']    
                                                                                                  
 pool2_relu (Activation)        (None, 56, 56, 256)  0           ['pool2_bn[0][0]']               
                                                                                                  
 pool2_conv (Conv2D)            (None, 56, 56, 128)  32768       ['pool2_relu[0][0]']             
                                                                                                  
 pool2_pool (AveragePooling2D)  (None, 28, 28, 128)  0           ['pool2_conv[0][0]']             
                                                                                                  
 conv3_block1_0_bn (BatchNormal  (None, 28, 28, 128)  512        ['pool2_pool[0][0]']             
 ization)                                                                                         
                                                                                                  
 conv3_block1_0_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  16384       ['conv3_block1_0_relu[0][0]']    
                                                                                                  
 conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block1_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block1_1_relu[0][0]']    
                                                                                                  
 conv3_block1_concat (Concatena  (None, 28, 28, 160)  0          ['pool2_pool[0][0]',             
 te)                                                              'conv3_block1_2_conv[0][0]']    
                                                                                                  
 conv3_block2_0_bn (BatchNormal  (None, 28, 28, 160)  640        ['conv3_block1_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_0_relu (Activatio  (None, 28, 28, 160)  0          ['conv3_block2_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  20480       ['conv3_block2_0_relu[0][0]']    
                                                                                                  
 conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block2_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block2_1_relu[0][0]']    
                                                                                                  
 conv3_block2_concat (Concatena  (None, 28, 28, 192)  0          ['conv3_block1_concat[0][0]',    
 te)                                                              'conv3_block2_2_conv[0][0]']    
                                                                                                  
 conv3_block3_0_bn (BatchNormal  (None, 28, 28, 192)  768        ['conv3_block2_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_0_relu (Activatio  (None, 28, 28, 192)  0          ['conv3_block3_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  24576       ['conv3_block3_0_relu[0][0]']    
                                                                                                  
 conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block3_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block3_1_relu[0][0]']    
                                                                                                  
 conv3_block3_concat (Concatena  (None, 28, 28, 224)  0          ['conv3_block2_concat[0][0]',    
 te)                                                              'conv3_block3_2_conv[0][0]']    
                                                                                                  
 conv3_block4_0_bn (BatchNormal  (None, 28, 28, 224)  896        ['conv3_block3_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_0_relu (Activatio  (None, 28, 28, 224)  0          ['conv3_block4_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  28672       ['conv3_block4_0_relu[0][0]']    
                                                                                                  
 conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block4_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block4_1_relu[0][0]']    
                                                                                                  
 conv3_block4_concat (Concatena  (None, 28, 28, 256)  0          ['conv3_block3_concat[0][0]',    
 te)                                                              'conv3_block4_2_conv[0][0]']    
                                                                                                  
 conv3_block5_0_bn (BatchNormal  (None, 28, 28, 256)  1024       ['conv3_block4_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block5_0_relu (Activatio  (None, 28, 28, 256)  0          ['conv3_block5_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block5_1_conv (Conv2D)   (None, 28, 28, 128)  32768       ['conv3_block5_0_relu[0][0]']    
                                                                                                  
 conv3_block5_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block5_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block5_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block5_1_relu[0][0]']    
                                                                                                  
 conv3_block5_concat (Concatena  (None, 28, 28, 288)  0          ['conv3_block4_concat[0][0]',    
 te)                                                              'conv3_block5_2_conv[0][0]']    
                                                                                                  
 conv3_block6_0_bn (BatchNormal  (None, 28, 28, 288)  1152       ['conv3_block5_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block6_0_relu (Activatio  (None, 28, 28, 288)  0          ['conv3_block6_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block6_1_conv (Conv2D)   (None, 28, 28, 128)  36864       ['conv3_block6_0_relu[0][0]']    
                                                                                                  
 conv3_block6_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block6_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block6_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block6_1_relu[0][0]']    
                                                                                                  
 conv3_block6_concat (Concatena  (None, 28, 28, 320)  0          ['conv3_block5_concat[0][0]',    
 te)                                                              'conv3_block6_2_conv[0][0]']    
                                                                                                  
 conv3_block7_0_bn (BatchNormal  (None, 28, 28, 320)  1280       ['conv3_block6_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block7_0_relu (Activatio  (None, 28, 28, 320)  0          ['conv3_block7_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block7_1_conv (Conv2D)   (None, 28, 28, 128)  40960       ['conv3_block7_0_relu[0][0]']    
                                                                                                  
 conv3_block7_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block7_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block7_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block7_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block7_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block7_1_relu[0][0]']    
                                                                                                  
 conv3_block7_concat (Concatena  (None, 28, 28, 352)  0          ['conv3_block6_concat[0][0]',    
 te)                                                              'conv3_block7_2_conv[0][0]']    
                                                                                                  
 conv3_block8_0_bn (BatchNormal  (None, 28, 28, 352)  1408       ['conv3_block7_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block8_0_relu (Activatio  (None, 28, 28, 352)  0          ['conv3_block8_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block8_1_conv (Conv2D)   (None, 28, 28, 128)  45056       ['conv3_block8_0_relu[0][0]']    
                                                                                                  
 conv3_block8_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block8_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block8_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block8_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block8_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block8_1_relu[0][0]']    
                                                                                                  
 conv3_block8_concat (Concatena  (None, 28, 28, 384)  0          ['conv3_block7_concat[0][0]',    
 te)                                                              'conv3_block8_2_conv[0][0]']    
                                                                                                  
 conv3_block9_0_bn (BatchNormal  (None, 28, 28, 384)  1536       ['conv3_block8_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block9_0_relu (Activatio  (None, 28, 28, 384)  0          ['conv3_block9_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block9_1_conv (Conv2D)   (None, 28, 28, 128)  49152       ['conv3_block9_0_relu[0][0]']    
                                                                                                  
 conv3_block9_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block9_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block9_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block9_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block9_2_conv (Conv2D)   (None, 28, 28, 32)   36864       ['conv3_block9_1_relu[0][0]']    
                                                                                                  
 conv3_block9_concat (Concatena  (None, 28, 28, 416)  0          ['conv3_block8_concat[0][0]',    
 te)                                                              'conv3_block9_2_conv[0][0]']    
                                                                                                  
 conv3_block10_0_bn (BatchNorma  (None, 28, 28, 416)  1664       ['conv3_block9_concat[0][0]']    
 lization)                                                                                        
                                                                                                  
 conv3_block10_0_relu (Activati  (None, 28, 28, 416)  0          ['conv3_block10_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block10_1_conv (Conv2D)  (None, 28, 28, 128)  53248       ['conv3_block10_0_relu[0][0]']   
                                                                                                  
 conv3_block10_1_bn (BatchNorma  (None, 28, 28, 128)  512        ['conv3_block10_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block10_1_relu (Activati  (None, 28, 28, 128)  0          ['conv3_block10_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block10_2_conv (Conv2D)  (None, 28, 28, 32)   36864       ['conv3_block10_1_relu[0][0]']   
                                                                                                  
 conv3_block10_concat (Concaten  (None, 28, 28, 448)  0          ['conv3_block9_concat[0][0]',    
 ate)                                                             'conv3_block10_2_conv[0][0]']   
                                                                                                  
 conv3_block11_0_bn (BatchNorma  (None, 28, 28, 448)  1792       ['conv3_block10_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block11_0_relu (Activati  (None, 28, 28, 448)  0          ['conv3_block11_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block11_1_conv (Conv2D)  (None, 28, 28, 128)  57344       ['conv3_block11_0_relu[0][0]']   
                                                                                                  
 conv3_block11_1_bn (BatchNorma  (None, 28, 28, 128)  512        ['conv3_block11_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block11_1_relu (Activati  (None, 28, 28, 128)  0          ['conv3_block11_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block11_2_conv (Conv2D)  (None, 28, 28, 32)   36864       ['conv3_block11_1_relu[0][0]']   
                                                                                                  
 conv3_block11_concat (Concaten  (None, 28, 28, 480)  0          ['conv3_block10_concat[0][0]',   
 ate)                                                             'conv3_block11_2_conv[0][0]']   
                                                                                                  
 conv3_block12_0_bn (BatchNorma  (None, 28, 28, 480)  1920       ['conv3_block11_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block12_0_relu (Activati  (None, 28, 28, 480)  0          ['conv3_block12_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block12_1_conv (Conv2D)  (None, 28, 28, 128)  61440       ['conv3_block12_0_relu[0][0]']   
                                                                                                  
 conv3_block12_1_bn (BatchNorma  (None, 28, 28, 128)  512        ['conv3_block12_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block12_1_relu (Activati  (None, 28, 28, 128)  0          ['conv3_block12_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block12_2_conv (Conv2D)  (None, 28, 28, 32)   36864       ['conv3_block12_1_relu[0][0]']   
                                                                                                  
 conv3_block12_concat (Concaten  (None, 28, 28, 512)  0          ['conv3_block11_concat[0][0]',   
 ate)                                                             'conv3_block12_2_conv[0][0]']   
                                                                                                  
 pool3_bn (BatchNormalization)  (None, 28, 28, 512)  2048        ['conv3_block12_concat[0][0]']   
                                                                                                  
 pool3_relu (Activation)        (None, 28, 28, 512)  0           ['pool3_bn[0][0]']               
                                                                                                  
 pool3_conv (Conv2D)            (None, 28, 28, 256)  131072      ['pool3_relu[0][0]']             
                                                                                                  
 pool3_pool (AveragePooling2D)  (None, 14, 14, 256)  0           ['pool3_conv[0][0]']             
                                                                                                  
 conv4_block1_0_bn (BatchNormal  (None, 14, 14, 256)  1024       ['pool3_pool[0][0]']             
 ization)                                                                                         
                                                                                                  
 conv4_block1_0_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block1_1_conv (Conv2D)   (None, 14, 14, 128)  32768       ['conv4_block1_0_relu[0][0]']    
                                                                                                  
 conv4_block1_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block1_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block1_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block1_1_relu[0][0]']    
                                                                                                  
 conv4_block1_concat (Concatena  (None, 14, 14, 288)  0          ['pool3_pool[0][0]',             
 te)                                                              'conv4_block1_2_conv[0][0]']    
                                                                                                  
 conv4_block2_0_bn (BatchNormal  (None, 14, 14, 288)  1152       ['conv4_block1_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block2_0_relu (Activatio  (None, 14, 14, 288)  0          ['conv4_block2_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block2_1_conv (Conv2D)   (None, 14, 14, 128)  36864       ['conv4_block2_0_relu[0][0]']    
                                                                                                  
 conv4_block2_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block2_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block2_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block2_1_relu[0][0]']    
                                                                                                  
 conv4_block2_concat (Concatena  (None, 14, 14, 320)  0          ['conv4_block1_concat[0][0]',    
 te)                                                              'conv4_block2_2_conv[0][0]']    
                                                                                                  
 conv4_block3_0_bn (BatchNormal  (None, 14, 14, 320)  1280       ['conv4_block2_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block3_0_relu (Activatio  (None, 14, 14, 320)  0          ['conv4_block3_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block3_1_conv (Conv2D)   (None, 14, 14, 128)  40960       ['conv4_block3_0_relu[0][0]']    
                                                                                                  
 conv4_block3_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block3_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block3_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block3_1_relu[0][0]']    
                                                                                                  
 conv4_block3_concat (Concatena  (None, 14, 14, 352)  0          ['conv4_block2_concat[0][0]',    
 te)                                                              'conv4_block3_2_conv[0][0]']    
                                                                                                  
 conv4_block4_0_bn (BatchNormal  (None, 14, 14, 352)  1408       ['conv4_block3_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block4_0_relu (Activatio  (None, 14, 14, 352)  0          ['conv4_block4_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block4_1_conv (Conv2D)   (None, 14, 14, 128)  45056       ['conv4_block4_0_relu[0][0]']    
                                                                                                  
 conv4_block4_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block4_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block4_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block4_1_relu[0][0]']    
                                                                                                  
 conv4_block4_concat (Concatena  (None, 14, 14, 384)  0          ['conv4_block3_concat[0][0]',    
 te)                                                              'conv4_block4_2_conv[0][0]']    
                                                                                                  
 conv4_block5_0_bn (BatchNormal  (None, 14, 14, 384)  1536       ['conv4_block4_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block5_0_relu (Activatio  (None, 14, 14, 384)  0          ['conv4_block5_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block5_1_conv (Conv2D)   (None, 14, 14, 128)  49152       ['conv4_block5_0_relu[0][0]']    
                                                                                                  
 conv4_block5_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block5_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block5_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block5_1_relu[0][0]']    
                                                                                                  
 conv4_block5_concat (Concatena  (None, 14, 14, 416)  0          ['conv4_block4_concat[0][0]',    
 te)                                                              'conv4_block5_2_conv[0][0]']    
                                                                                                  
 conv4_block6_0_bn (BatchNormal  (None, 14, 14, 416)  1664       ['conv4_block5_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block6_0_relu (Activatio  (None, 14, 14, 416)  0          ['conv4_block6_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block6_1_conv (Conv2D)   (None, 14, 14, 128)  53248       ['conv4_block6_0_relu[0][0]']    
                                                                                                  
 conv4_block6_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block6_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block6_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block6_1_relu[0][0]']    
                                                                                                  
 conv4_block6_concat (Concatena  (None, 14, 14, 448)  0          ['conv4_block5_concat[0][0]',    
 te)                                                              'conv4_block6_2_conv[0][0]']    
                                                                                                  
 conv4_block7_0_bn (BatchNormal  (None, 14, 14, 448)  1792       ['conv4_block6_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block7_0_relu (Activatio  (None, 14, 14, 448)  0          ['conv4_block7_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block7_1_conv (Conv2D)   (None, 14, 14, 128)  57344       ['conv4_block7_0_relu[0][0]']    
                                                                                                  
 conv4_block7_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block7_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block7_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block7_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block7_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block7_1_relu[0][0]']    
                                                                                                  
 conv4_block7_concat (Concatena  (None, 14, 14, 480)  0          ['conv4_block6_concat[0][0]',    
 te)                                                              'conv4_block7_2_conv[0][0]']    
                                                                                                  
 conv4_block8_0_bn (BatchNormal  (None, 14, 14, 480)  1920       ['conv4_block7_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block8_0_relu (Activatio  (None, 14, 14, 480)  0          ['conv4_block8_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block8_1_conv (Conv2D)   (None, 14, 14, 128)  61440       ['conv4_block8_0_relu[0][0]']    
                                                                                                  
 conv4_block8_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block8_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block8_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block8_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block8_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block8_1_relu[0][0]']    
                                                                                                  
 conv4_block8_concat (Concatena  (None, 14, 14, 512)  0          ['conv4_block7_concat[0][0]',    
 te)                                                              'conv4_block8_2_conv[0][0]']    
                                                                                                  
 conv4_block9_0_bn (BatchNormal  (None, 14, 14, 512)  2048       ['conv4_block8_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block9_0_relu (Activatio  (None, 14, 14, 512)  0          ['conv4_block9_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block9_1_conv (Conv2D)   (None, 14, 14, 128)  65536       ['conv4_block9_0_relu[0][0]']    
                                                                                                  
 conv4_block9_1_bn (BatchNormal  (None, 14, 14, 128)  512        ['conv4_block9_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block9_1_relu (Activatio  (None, 14, 14, 128)  0          ['conv4_block9_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block9_2_conv (Conv2D)   (None, 14, 14, 32)   36864       ['conv4_block9_1_relu[0][0]']    
                                                                                                  
 conv4_block9_concat (Concatena  (None, 14, 14, 544)  0          ['conv4_block8_concat[0][0]',    
 te)                                                              'conv4_block9_2_conv[0][0]']    
                                                                                                  
 conv4_block10_0_bn (BatchNorma  (None, 14, 14, 544)  2176       ['conv4_block9_concat[0][0]']    
 lization)                                                                                        
                                                                                                  
 conv4_block10_0_relu (Activati  (None, 14, 14, 544)  0          ['conv4_block10_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block10_1_conv (Conv2D)  (None, 14, 14, 128)  69632       ['conv4_block10_0_relu[0][0]']   
                                                                                                  
 conv4_block10_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block10_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block10_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block10_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block10_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block10_1_relu[0][0]']   
                                                                                                  
 conv4_block10_concat (Concaten  (None, 14, 14, 576)  0          ['conv4_block9_concat[0][0]',    
 ate)                                                             'conv4_block10_2_conv[0][0]']   
                                                                                                  
 conv4_block11_0_bn (BatchNorma  (None, 14, 14, 576)  2304       ['conv4_block10_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block11_0_relu (Activati  (None, 14, 14, 576)  0          ['conv4_block11_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block11_1_conv (Conv2D)  (None, 14, 14, 128)  73728       ['conv4_block11_0_relu[0][0]']   
                                                                                                  
 conv4_block11_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block11_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block11_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block11_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block11_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block11_1_relu[0][0]']   
                                                                                                  
 conv4_block11_concat (Concaten  (None, 14, 14, 608)  0          ['conv4_block10_concat[0][0]',   
 ate)                                                             'conv4_block11_2_conv[0][0]']   
                                                                                                  
 conv4_block12_0_bn (BatchNorma  (None, 14, 14, 608)  2432       ['conv4_block11_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block12_0_relu (Activati  (None, 14, 14, 608)  0          ['conv4_block12_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block12_1_conv (Conv2D)  (None, 14, 14, 128)  77824       ['conv4_block12_0_relu[0][0]']   
                                                                                                  
 conv4_block12_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block12_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block12_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block12_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block12_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block12_1_relu[0][0]']   
                                                                                                  
 conv4_block12_concat (Concaten  (None, 14, 14, 640)  0          ['conv4_block11_concat[0][0]',   
 ate)                                                             'conv4_block12_2_conv[0][0]']   
                                                                                                  
 conv4_block13_0_bn (BatchNorma  (None, 14, 14, 640)  2560       ['conv4_block12_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block13_0_relu (Activati  (None, 14, 14, 640)  0          ['conv4_block13_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block13_1_conv (Conv2D)  (None, 14, 14, 128)  81920       ['conv4_block13_0_relu[0][0]']   
                                                                                                  
 conv4_block13_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block13_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block13_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block13_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block13_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block13_1_relu[0][0]']   
                                                                                                  
 conv4_block13_concat (Concaten  (None, 14, 14, 672)  0          ['conv4_block12_concat[0][0]',   
 ate)                                                             'conv4_block13_2_conv[0][0]']   
                                                                                                  
 conv4_block14_0_bn (BatchNorma  (None, 14, 14, 672)  2688       ['conv4_block13_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block14_0_relu (Activati  (None, 14, 14, 672)  0          ['conv4_block14_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block14_1_conv (Conv2D)  (None, 14, 14, 128)  86016       ['conv4_block14_0_relu[0][0]']   
                                                                                                  
 conv4_block14_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block14_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block14_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block14_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block14_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block14_1_relu[0][0]']   
                                                                                                  
 conv4_block14_concat (Concaten  (None, 14, 14, 704)  0          ['conv4_block13_concat[0][0]',   
 ate)                                                             'conv4_block14_2_conv[0][0]']   
                                                                                                  
 conv4_block15_0_bn (BatchNorma  (None, 14, 14, 704)  2816       ['conv4_block14_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block15_0_relu (Activati  (None, 14, 14, 704)  0          ['conv4_block15_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block15_1_conv (Conv2D)  (None, 14, 14, 128)  90112       ['conv4_block15_0_relu[0][0]']   
                                                                                                  
 conv4_block15_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block15_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block15_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block15_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block15_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block15_1_relu[0][0]']   
                                                                                                  
 conv4_block15_concat (Concaten  (None, 14, 14, 736)  0          ['conv4_block14_concat[0][0]',   
 ate)                                                             'conv4_block15_2_conv[0][0]']   
                                                                                                  
 conv4_block16_0_bn (BatchNorma  (None, 14, 14, 736)  2944       ['conv4_block15_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block16_0_relu (Activati  (None, 14, 14, 736)  0          ['conv4_block16_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block16_1_conv (Conv2D)  (None, 14, 14, 128)  94208       ['conv4_block16_0_relu[0][0]']   
                                                                                                  
 conv4_block16_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block16_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block16_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block16_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block16_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block16_1_relu[0][0]']   
                                                                                                  
 conv4_block16_concat (Concaten  (None, 14, 14, 768)  0          ['conv4_block15_concat[0][0]',   
 ate)                                                             'conv4_block16_2_conv[0][0]']   
                                                                                                  
 conv4_block17_0_bn (BatchNorma  (None, 14, 14, 768)  3072       ['conv4_block16_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block17_0_relu (Activati  (None, 14, 14, 768)  0          ['conv4_block17_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block17_1_conv (Conv2D)  (None, 14, 14, 128)  98304       ['conv4_block17_0_relu[0][0]']   
                                                                                                  
 conv4_block17_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block17_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block17_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block17_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block17_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block17_1_relu[0][0]']   
                                                                                                  
 conv4_block17_concat (Concaten  (None, 14, 14, 800)  0          ['conv4_block16_concat[0][0]',   
 ate)                                                             'conv4_block17_2_conv[0][0]']   
                                                                                                  
 conv4_block18_0_bn (BatchNorma  (None, 14, 14, 800)  3200       ['conv4_block17_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block18_0_relu (Activati  (None, 14, 14, 800)  0          ['conv4_block18_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block18_1_conv (Conv2D)  (None, 14, 14, 128)  102400      ['conv4_block18_0_relu[0][0]']   
                                                                                                  
 conv4_block18_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block18_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block18_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block18_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block18_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block18_1_relu[0][0]']   
                                                                                                  
 conv4_block18_concat (Concaten  (None, 14, 14, 832)  0          ['conv4_block17_concat[0][0]',   
 ate)                                                             'conv4_block18_2_conv[0][0]']   
                                                                                                  
 conv4_block19_0_bn (BatchNorma  (None, 14, 14, 832)  3328       ['conv4_block18_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block19_0_relu (Activati  (None, 14, 14, 832)  0          ['conv4_block19_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block19_1_conv (Conv2D)  (None, 14, 14, 128)  106496      ['conv4_block19_0_relu[0][0]']   
                                                                                                  
 conv4_block19_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block19_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block19_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block19_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block19_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block19_1_relu[0][0]']   
                                                                                                  
 conv4_block19_concat (Concaten  (None, 14, 14, 864)  0          ['conv4_block18_concat[0][0]',   
 ate)                                                             'conv4_block19_2_conv[0][0]']   
                                                                                                  
 conv4_block20_0_bn (BatchNorma  (None, 14, 14, 864)  3456       ['conv4_block19_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block20_0_relu (Activati  (None, 14, 14, 864)  0          ['conv4_block20_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block20_1_conv (Conv2D)  (None, 14, 14, 128)  110592      ['conv4_block20_0_relu[0][0]']   
                                                                                                  
 conv4_block20_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block20_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block20_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block20_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block20_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block20_1_relu[0][0]']   
                                                                                                  
 conv4_block20_concat (Concaten  (None, 14, 14, 896)  0          ['conv4_block19_concat[0][0]',   
 ate)                                                             'conv4_block20_2_conv[0][0]']   
                                                                                                  
 conv4_block21_0_bn (BatchNorma  (None, 14, 14, 896)  3584       ['conv4_block20_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block21_0_relu (Activati  (None, 14, 14, 896)  0          ['conv4_block21_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block21_1_conv (Conv2D)  (None, 14, 14, 128)  114688      ['conv4_block21_0_relu[0][0]']   
                                                                                                  
 conv4_block21_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block21_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block21_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block21_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block21_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block21_1_relu[0][0]']   
                                                                                                  
 conv4_block21_concat (Concaten  (None, 14, 14, 928)  0          ['conv4_block20_concat[0][0]',   
 ate)                                                             'conv4_block21_2_conv[0][0]']   
                                                                                                  
 conv4_block22_0_bn (BatchNorma  (None, 14, 14, 928)  3712       ['conv4_block21_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block22_0_relu (Activati  (None, 14, 14, 928)  0          ['conv4_block22_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block22_1_conv (Conv2D)  (None, 14, 14, 128)  118784      ['conv4_block22_0_relu[0][0]']   
                                                                                                  
 conv4_block22_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block22_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block22_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block22_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block22_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block22_1_relu[0][0]']   
                                                                                                  
 conv4_block22_concat (Concaten  (None, 14, 14, 960)  0          ['conv4_block21_concat[0][0]',   
 ate)                                                             'conv4_block22_2_conv[0][0]']   
                                                                                                  
 conv4_block23_0_bn (BatchNorma  (None, 14, 14, 960)  3840       ['conv4_block22_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block23_0_relu (Activati  (None, 14, 14, 960)  0          ['conv4_block23_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block23_1_conv (Conv2D)  (None, 14, 14, 128)  122880      ['conv4_block23_0_relu[0][0]']   
                                                                                                  
 conv4_block23_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block23_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block23_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block23_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block23_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block23_1_relu[0][0]']   
                                                                                                  
 conv4_block23_concat (Concaten  (None, 14, 14, 992)  0          ['conv4_block22_concat[0][0]',   
 ate)                                                             'conv4_block23_2_conv[0][0]']   
                                                                                                  
 conv4_block24_0_bn (BatchNorma  (None, 14, 14, 992)  3968       ['conv4_block23_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block24_0_relu (Activati  (None, 14, 14, 992)  0          ['conv4_block24_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block24_1_conv (Conv2D)  (None, 14, 14, 128)  126976      ['conv4_block24_0_relu[0][0]']   
                                                                                                  
 conv4_block24_1_bn (BatchNorma  (None, 14, 14, 128)  512        ['conv4_block24_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block24_1_relu (Activati  (None, 14, 14, 128)  0          ['conv4_block24_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block24_2_conv (Conv2D)  (None, 14, 14, 32)   36864       ['conv4_block24_1_relu[0][0]']   
                                                                                                  
 conv4_block24_concat (Concaten  (None, 14, 14, 1024  0          ['conv4_block23_concat[0][0]',   
 ate)                           )                                 'conv4_block24_2_conv[0][0]']   
                                                                                                  
 pool4_bn (BatchNormalization)  (None, 14, 14, 1024  4096        ['conv4_block24_concat[0][0]']   
                                )                                                                 
                                                                                                  
 pool4_relu (Activation)        (None, 14, 14, 1024  0           ['pool4_bn[0][0]']               
                                )                                                                 
                                                                                                  
 pool4_conv (Conv2D)            (None, 14, 14, 512)  524288      ['pool4_relu[0][0]']             
                                                                                                  
 pool4_pool (AveragePooling2D)  (None, 7, 7, 512)    0           ['pool4_conv[0][0]']             
                                                                                                  
 conv5_block1_0_bn (BatchNormal  (None, 7, 7, 512)   2048        ['pool4_pool[0][0]']             
 ization)                                                                                         
                                                                                                  
 conv5_block1_0_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block1_1_conv (Conv2D)   (None, 7, 7, 128)    65536       ['conv5_block1_0_relu[0][0]']    
                                                                                                  
 conv5_block1_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block1_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block1_1_relu[0][0]']    
                                                                                                  
 conv5_block1_concat (Concatena  (None, 7, 7, 544)   0           ['pool4_pool[0][0]',             
 te)                                                              'conv5_block1_2_conv[0][0]']    
                                                                                                  
 conv5_block2_0_bn (BatchNormal  (None, 7, 7, 544)   2176        ['conv5_block1_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_0_relu (Activatio  (None, 7, 7, 544)   0           ['conv5_block2_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block2_1_conv (Conv2D)   (None, 7, 7, 128)    69632       ['conv5_block2_0_relu[0][0]']    
                                                                                                  
 conv5_block2_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block2_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block2_1_relu[0][0]']    
                                                                                                  
 conv5_block2_concat (Concatena  (None, 7, 7, 576)   0           ['conv5_block1_concat[0][0]',    
 te)                                                              'conv5_block2_2_conv[0][0]']    
                                                                                                  
 conv5_block3_0_bn (BatchNormal  (None, 7, 7, 576)   2304        ['conv5_block2_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_0_relu (Activatio  (None, 7, 7, 576)   0           ['conv5_block3_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block3_1_conv (Conv2D)   (None, 7, 7, 128)    73728       ['conv5_block3_0_relu[0][0]']    
                                                                                                  
 conv5_block3_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block3_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block3_1_relu[0][0]']    
                                                                                                  
 conv5_block3_concat (Concatena  (None, 7, 7, 608)   0           ['conv5_block2_concat[0][0]',    
 te)                                                              'conv5_block3_2_conv[0][0]']    
                                                                                                  
 conv5_block4_0_bn (BatchNormal  (None, 7, 7, 608)   2432        ['conv5_block3_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block4_0_relu (Activatio  (None, 7, 7, 608)   0           ['conv5_block4_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block4_1_conv (Conv2D)   (None, 7, 7, 128)    77824       ['conv5_block4_0_relu[0][0]']    
                                                                                                  
 conv5_block4_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block4_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block4_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block4_1_relu[0][0]']    
                                                                                                  
 conv5_block4_concat (Concatena  (None, 7, 7, 640)   0           ['conv5_block3_concat[0][0]',    
 te)                                                              'conv5_block4_2_conv[0][0]']    
                                                                                                  
 conv5_block5_0_bn (BatchNormal  (None, 7, 7, 640)   2560        ['conv5_block4_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block5_0_relu (Activatio  (None, 7, 7, 640)   0           ['conv5_block5_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block5_1_conv (Conv2D)   (None, 7, 7, 128)    81920       ['conv5_block5_0_relu[0][0]']    
                                                                                                  
 conv5_block5_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block5_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block5_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block5_1_relu[0][0]']    
                                                                                                  
 conv5_block5_concat (Concatena  (None, 7, 7, 672)   0           ['conv5_block4_concat[0][0]',    
 te)                                                              'conv5_block5_2_conv[0][0]']    
                                                                                                  
 conv5_block6_0_bn (BatchNormal  (None, 7, 7, 672)   2688        ['conv5_block5_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block6_0_relu (Activatio  (None, 7, 7, 672)   0           ['conv5_block6_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block6_1_conv (Conv2D)   (None, 7, 7, 128)    86016       ['conv5_block6_0_relu[0][0]']    
                                                                                                  
 conv5_block6_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block6_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block6_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block6_1_relu[0][0]']    
                                                                                                  
 conv5_block6_concat (Concatena  (None, 7, 7, 704)   0           ['conv5_block5_concat[0][0]',    
 te)                                                              'conv5_block6_2_conv[0][0]']    
                                                                                                  
 conv5_block7_0_bn (BatchNormal  (None, 7, 7, 704)   2816        ['conv5_block6_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block7_0_relu (Activatio  (None, 7, 7, 704)   0           ['conv5_block7_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block7_1_conv (Conv2D)   (None, 7, 7, 128)    90112       ['conv5_block7_0_relu[0][0]']    
                                                                                                  
 conv5_block7_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block7_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block7_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block7_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block7_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block7_1_relu[0][0]']    
                                                                                                  
 conv5_block7_concat (Concatena  (None, 7, 7, 736)   0           ['conv5_block6_concat[0][0]',    
 te)                                                              'conv5_block7_2_conv[0][0]']    
                                                                                                  
 conv5_block8_0_bn (BatchNormal  (None, 7, 7, 736)   2944        ['conv5_block7_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block8_0_relu (Activatio  (None, 7, 7, 736)   0           ['conv5_block8_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block8_1_conv (Conv2D)   (None, 7, 7, 128)    94208       ['conv5_block8_0_relu[0][0]']    
                                                                                                  
 conv5_block8_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block8_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block8_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block8_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block8_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block8_1_relu[0][0]']    
                                                                                                  
 conv5_block8_concat (Concatena  (None, 7, 7, 768)   0           ['conv5_block7_concat[0][0]',    
 te)                                                              'conv5_block8_2_conv[0][0]']    
                                                                                                  
 conv5_block9_0_bn (BatchNormal  (None, 7, 7, 768)   3072        ['conv5_block8_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block9_0_relu (Activatio  (None, 7, 7, 768)   0           ['conv5_block9_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block9_1_conv (Conv2D)   (None, 7, 7, 128)    98304       ['conv5_block9_0_relu[0][0]']    
                                                                                                  
 conv5_block9_1_bn (BatchNormal  (None, 7, 7, 128)   512         ['conv5_block9_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block9_1_relu (Activatio  (None, 7, 7, 128)   0           ['conv5_block9_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block9_2_conv (Conv2D)   (None, 7, 7, 32)     36864       ['conv5_block9_1_relu[0][0]']    
                                                                                                  
 conv5_block9_concat (Concatena  (None, 7, 7, 800)   0           ['conv5_block8_concat[0][0]',    
 te)                                                              'conv5_block9_2_conv[0][0]']    
                                                                                                  
 conv5_block10_0_bn (BatchNorma  (None, 7, 7, 800)   3200        ['conv5_block9_concat[0][0]']    
 lization)                                                                                        
                                                                                                  
 conv5_block10_0_relu (Activati  (None, 7, 7, 800)   0           ['conv5_block10_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block10_1_conv (Conv2D)  (None, 7, 7, 128)    102400      ['conv5_block10_0_relu[0][0]']   
                                                                                                  
 conv5_block10_1_bn (BatchNorma  (None, 7, 7, 128)   512         ['conv5_block10_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block10_1_relu (Activati  (None, 7, 7, 128)   0           ['conv5_block10_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block10_2_conv (Conv2D)  (None, 7, 7, 32)     36864       ['conv5_block10_1_relu[0][0]']   
                                                                                                  
 conv5_block10_concat (Concaten  (None, 7, 7, 832)   0           ['conv5_block9_concat[0][0]',    
 ate)                                                             'conv5_block10_2_conv[0][0]']   
                                                                                                  
 conv5_block11_0_bn (BatchNorma  (None, 7, 7, 832)   3328        ['conv5_block10_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block11_0_relu (Activati  (None, 7, 7, 832)   0           ['conv5_block11_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block11_1_conv (Conv2D)  (None, 7, 7, 128)    106496      ['conv5_block11_0_relu[0][0]']   
                                                                                                  
 conv5_block11_1_bn (BatchNorma  (None, 7, 7, 128)   512         ['conv5_block11_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block11_1_relu (Activati  (None, 7, 7, 128)   0           ['conv5_block11_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block11_2_conv (Conv2D)  (None, 7, 7, 32)     36864       ['conv5_block11_1_relu[0][0]']   
                                                                                                  
 conv5_block11_concat (Concaten  (None, 7, 7, 864)   0           ['conv5_block10_concat[0][0]',   
 ate)                                                             'conv5_block11_2_conv[0][0]']   
                                                                                                  
 conv5_block12_0_bn (BatchNorma  (None, 7, 7, 864)   3456        ['conv5_block11_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block12_0_relu (Activati  (None, 7, 7, 864)   0           ['conv5_block12_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block12_1_conv (Conv2D)  (None, 7, 7, 128)    110592      ['conv5_block12_0_relu[0][0]']   
                                                                                                  
 conv5_block12_1_bn (BatchNorma  (None, 7, 7, 128)   512         ['conv5_block12_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block12_1_relu (Activati  (None, 7, 7, 128)   0           ['conv5_block12_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block12_2_conv (Conv2D)  (None, 7, 7, 32)     36864       ['conv5_block12_1_relu[0][0]']   
                                                                                                  
 conv5_block12_concat (Concaten  (None, 7, 7, 896)   0           ['conv5_block11_concat[0][0]',   
 ate)                                                             'conv5_block12_2_conv[0][0]']   
                                                                                                  
 conv5_block13_0_bn (BatchNorma  (None, 7, 7, 896)   3584        ['conv5_block12_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block13_0_relu (Activati  (None, 7, 7, 896)   0           ['conv5_block13_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block13_1_conv (Conv2D)  (None, 7, 7, 128)    114688      ['conv5_block13_0_relu[0][0]']   
                                                                                                  
 conv5_block13_1_bn (BatchNorma  (None, 7, 7, 128)   512         ['conv5_block13_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block13_1_relu (Activati  (None, 7, 7, 128)   0           ['conv5_block13_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block13_2_conv (Conv2D)  (None, 7, 7, 32)     36864       ['conv5_block13_1_relu[0][0]']   
                                                                                                  
 conv5_block13_concat (Concaten  (None, 7, 7, 928)   0           ['conv5_block12_concat[0][0]',   
 ate)                                                             'conv5_block13_2_conv[0][0]']   
                                                                                                  
 conv5_block14_0_bn (BatchNorma  (None, 7, 7, 928)   3712        ['conv5_block13_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block14_0_relu (Activati  (None, 7, 7, 928)   0           ['conv5_block14_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block14_1_conv (Conv2D)  (None, 7, 7, 128)    118784      ['conv5_block14_0_relu[0][0]']   
                                                                                                  
 conv5_block14_1_bn (BatchNorma  (None, 7, 7, 128)   512         ['conv5_block14_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block14_1_relu (Activati  (None, 7, 7, 128)   0           ['conv5_block14_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block14_2_conv (Conv2D)  (None, 7, 7, 32)     36864       ['conv5_block14_1_relu[0][0]']   
                                                                                                  
 conv5_block14_concat (Concaten  (None, 7, 7, 960)   0           ['conv5_block13_concat[0][0]',   
 ate)                                                             'conv5_block14_2_conv[0][0]']   
                                                                                                  
 conv5_block15_0_bn (BatchNorma  (None, 7, 7, 960)   3840        ['conv5_block14_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block15_0_relu (Activati  (None, 7, 7, 960)   0           ['conv5_block15_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block15_1_conv (Conv2D)  (None, 7, 7, 128)    122880      ['conv5_block15_0_relu[0][0]']   
                                                                                                  
 conv5_block15_1_bn (BatchNorma  (None, 7, 7, 128)   512         ['conv5_block15_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block15_1_relu (Activati  (None, 7, 7, 128)   0           ['conv5_block15_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block15_2_conv (Conv2D)  (None, 7, 7, 32)     36864       ['conv5_block15_1_relu[0][0]']   
                                                                                                  
 conv5_block15_concat (Concaten  (None, 7, 7, 992)   0           ['conv5_block14_concat[0][0]',   
 ate)                                                             'conv5_block15_2_conv[0][0]']   
                                                                                                  
 conv5_block16_0_bn (BatchNorma  (None, 7, 7, 992)   3968        ['conv5_block15_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block16_0_relu (Activati  (None, 7, 7, 992)   0           ['conv5_block16_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block16_1_conv (Conv2D)  (None, 7, 7, 128)    126976      ['conv5_block16_0_relu[0][0]']   
                                                                                                  
 conv5_block16_1_bn (BatchNorma  (None, 7, 7, 128)   512         ['conv5_block16_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block16_1_relu (Activati  (None, 7, 7, 128)   0           ['conv5_block16_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block16_2_conv (Conv2D)  (None, 7, 7, 32)     36864       ['conv5_block16_1_relu[0][0]']   
                                                                                                  
 conv5_block16_concat (Concaten  (None, 7, 7, 1024)  0           ['conv5_block15_concat[0][0]',   
 ate)                                                             'conv5_block16_2_conv[0][0]']   
                                                                                                  
 bn (BatchNormalization)        (None, 7, 7, 1024)   4096        ['conv5_block16_concat[0][0]']   
                                                                                                  
 relu (Activation)              (None, 7, 7, 1024)   0           ['bn[0][0]']                     
                                                                                                  
 avg_pool (GlobalAveragePooling  (None, 1024)        0           ['relu[0][0]']                   
 2D)                                                                                              
                                                                                                  
 dense (Dense)                  (None, 256)          262400      ['avg_pool[0][0]']               
                                                                                                  
 batch_normalization (BatchNorm  (None, 256)         1024        ['dense[0][0]']                  
 alization)                                                                                       
                                                                                                  
 tf.nn.relu (TFOpLambda)        (None, 256)          0           ['batch_normalization[0][0]']    
                                                                                                  
 dropout (Dropout)              (None, 256)          0           ['tf.nn.relu[0][0]']             
                                                                                                  
 dense_1 (Dense)                (None, 128)          32896       ['dropout[0][0]']                
                                                                                                  
 batch_normalization_1 (BatchNo  (None, 128)         512         ['dense_1[0][0]']                
 rmalization)                                                                                     
                                                                                                  
 tf.nn.relu_1 (TFOpLambda)      (None, 128)          0           ['batch_normalization_1[0][0]']  
                                                                                                  
 predictions (Dense)            (None, 2)            258         ['tf.nn.relu_1[0][0]']           
                                                                                                  
==================================================================================================
Total params: 7,334,594
Trainable params: 7,250,178
Non-trainable params: 84,416
__________________________________________________________________________________________________


checkpoint = ModelCheckpoint(filepath= 'Densenet_tuned.h5', save_best_only=True, save_weights_only=False)
lr_reduce = ReduceLROnPlateau(monitor='val_loss', factor=0.99, patience=2, verbose=2, mode='min')
early_stop = EarlyStopping(monitor='val_loss', min_delta=0, patience=2, mode='min')


optimizer = Adam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
tf.debugging.set_log_device_placement(True)
try:
    with tf.device('/device:XLA_GPU:0'):
        tf.keras.backend.clear_session()
        new_dense_model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['acc'])
except RuntimeError as e:
    print(e)


tf.keras.backend.clear_session()
batch_size = 60
nb_epochs = 10
new_dense_hist_1 = new_dense_model.fit(X_train1, y_train1,
                                       batch_size=batch_size,
                                       epochs=nb_epochs, 
                                       validation_data=(X_val, y_val),
                                       initial_epoch=0, callbacks=([checkpoint, lr_reduce, early_stop]))

Epoch 1/12
64/64 [==============================] - 224s 3s/step - loss: 0.6741 - acc: 0.6227 - val_loss: 1.2947 - val_acc: 0.5000 - lr: 1.0000e-04
Epoch 2/12
64/64 [==============================] - 250s 4s/step - loss: 0.6351 - acc: 0.6602 - val_loss: 0.9560 - val_acc: 0.4885 - lr: 1.0000e-04
Epoch 3/12
64/64 [==============================] - 235s 4s/step - loss: 0.6230 - acc: 0.6721 - val_loss: 0.7419 - val_acc: 0.5417 - lr: 1.0000e-04
Epoch 4/12
64/64 [==============================] - 228s 4s/step - loss: 0.6200 - acc: 0.6779 - val_loss: 0.7245 - val_acc: 0.5646 - lr: 1.0000e-04
Epoch 5/12
64/64 [==============================] - 238s 4s/step - loss: 0.6095 - acc: 0.6875 - val_loss: 0.7969 - val_acc: 0.5333 - lr: 1.0000e-04
Epoch 6/12
64/64 [==============================] - ETA: 0s - loss: 0.6072 - acc: 0.6854
Epoch 6: ReduceLROnPlateau reducing learning rate to 9.899999749904965e-05.
64/64 [==============================] - 239s 4s/step - loss: 0.6072 - acc: 0.6854 - val_loss: 0.8147 - val_acc: 0.5344 - lr: 1.0000e-04


pkl.dump(new_dense_model, open('new_dense_model_2.pkl', 'wb'))


# Pickling the densenet121 model with ChexNet weights:
# On every run, model metrics are being changed.
# We have already pickled the model, where we got the best accuracy so to avoid re-run of the model.
# Due to resource constraints, we have also executed the DenseNet121 model with ChexNet weights in colab pro using 12GB of GPU
# In Colab Pro, DenseNet121 has given us the best accuracy of more than 80% which was pickled and used in This project

load_options = tf.saved_model.LoadOptions(experimental_io_device='/job:localhost')

new_dense_model_2 = pickle.load(open('new_dense_model_2.pkl', 'rb'))
#new_dense_model_1.predict(X_test1)


Pre_trained_results.columns = ["Model_Name", "Accuracy", "Precision", "Recall", "F1 Score", "ROC"]


Pre_trained_results.style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()


train_label_pneumonia = train_label[train_label["Target"] == 1]

train_label_pneumonia = train_label_pneumonia.reset_index(drop = True)


train_label_pneumonia["Full_filename"] = "stage_2_train_images"+"/"+train_label_pneumonia["patientId"]+".dcm"

train_label_pneumonia["Filename"] = train_label_pneumonia["patientId"]+".dcm"

#train_label_pneumonia = train_label_pneumonia.drop(columns = ["patientId"])


train_4500_rows = train_label_pneumonia[:4500]


train_4500_rows.sample(2)


train_y_4500_box = train_4500_rows.drop(columns = ["Full_filename","Filename","Target","patientId"])


train_y_4500_box.columns

Index(['x', 'y', 'width', 'height'], dtype='object')


y_4500 = train_y_4500_box.copy()


img_rows=224
img_cols=224
dim = (img_rows, img_cols)

X_opac = []
brk = 0
i = 1 # initialisation
for img in tqdm(train_4500_rows["Full_filename"].values): 
    
    ds_3 = dicom.dcmread(img)
    img_3 = ds_3.pixel_array
    rgb = apply_color_lut(img_3, palette='PET')
    train_img = rgb
    try:
        train_img_resize = cv2.resize(train_img, dim, interpolation=cv2.INTER_LINEAR)
    except:
        brk +=1
        print("breaking out for",img)
        break
    height_2, width_2, layers = train_img_resize.shape
    size=(width_2,height_2)
    X_opac.append(train_img_resize)
    i += 1

100%|██████████████████████████████████████████████████████████████████████████████| 4500/4500 [01:50<00:00, 40.68it/s]


X_opac = np.asarray(X_opac)


fileName = "X_4500.pkl"
fileObject = open(fileName, 'wb')

pkl.dump(X_opac, fileObject)
fileObject.close()


y_4500.to_csv("y_4500.csv", index = False)


train_X_4500_3 = open("X_4500.pkl", "rb")
X_3_45k = pkl.load(train_X_4500_3)
X_3_45k[0:2]

[array([[[ 0,  4,  3],
         [ 0,  6,  5],
         [ 0,  5,  4],
         ...,
         [ 0,  5,  4],
         [ 0,  5,  4],
         [ 0,  5,  4]],
 
        [[ 0,  5,  4],
         [ 0,  6,  5],
         [ 0,  6,  5],
         ...,
         [ 0,  6,  5],
         [ 0,  6,  5],
         [ 0,  6,  5]],
 
        [[ 0,  5,  4],
         [ 0,  6,  5],
         [ 0,  6,  5],
         ...,
         [ 0,  6,  5],
         [ 0,  6,  5],
         [ 0,  6,  5]],
 
        ...,
 
        [[ 0,  5,  4],
         [ 0,  8,  7],
         [ 0, 10,  9],
         ...,
         [ 0, 12, 11],
         [ 0, 12, 11],
         [ 0, 12, 11]],
 
        [[ 0,  9,  8],
         [ 0,  9,  9],
         [ 0, 10,  9],
         ...,
         [ 0, 12, 11],
         [ 0, 12, 11],
         [ 0, 12, 11]],
 
        [[ 0,  7,  6],
         [ 0,  8,  7],
         [ 0, 11, 10],
         ...,
         [ 0, 11, 10],
         [ 0, 13, 12],
         [ 0, 13, 12]]], dtype=uint8),
 array([[[ 0,  4,  3],
         [ 0,  6,  5],
         [ 0,  5,  4],
         ...,
         [ 0,  5,  4],
         [ 0,  5,  4],
         [ 0,  5,  4]],
 
        [[ 0,  5,  4],
         [ 0,  6,  5],
         [ 0,  6,  5],
         ...,
         [ 0,  6,  5],
         [ 0,  6,  5],
         [ 0,  6,  5]],
 
        [[ 0,  5,  4],
         [ 0,  6,  5],
         [ 0,  6,  5],
         ...,
         [ 0,  6,  5],
         [ 0,  6,  5],
         [ 0,  6,  5]],
 
        ...,
 
        [[ 0,  5,  4],
         [ 0,  8,  7],
         [ 0, 10,  9],
         ...,
         [ 0, 12, 11],
         [ 0, 12, 11],
         [ 0, 12, 11]],
 
        [[ 0,  9,  8],
         [ 0,  9,  9],
         [ 0, 10,  9],
         ...,
         [ 0, 12, 11],
         [ 0, 12, 11],
         [ 0, 12, 11]],
 
        [[ 0,  7,  6],
         [ 0,  8,  7],
         [ 0, 11, 10],
         ...,
         [ 0, 11, 10],
         [ 0, 13, 12],
         [ 0, 13, 12]]], dtype=uint8)]


X_3_45k = np.array(X_3_45k)


X_3_45k.shape

(4500, 224, 224, 3)


y_train = train_y_4500_box.to_numpy()


y_train.shape

(4500, 4)


train_4500_rows.iloc[234,:]

patientId                                 084d42b9-0a43-490d-9a8d-e4d7545c44f5
x                                                                        513.0
y                                                                        472.0
width                                                                    320.0
height                                                                   342.0
Target                                                                       1
Full_filename    stage_2_train_images/084d42b9-0a43-490d-9a8d-e4d7545c44f5.dcm
Filename                              084d42b9-0a43-490d-9a8d-e4d7545c44f5.dcm
Name: 234, dtype: object


X_train_bb = X_3_45k.copy()


y_train_bb = y_train.copy()


print(X_train_bb.shape)
print(y_train_bb.shape)

(4500, 224, 224, 3)
(4500, 4)


rand_image = 234

# Pick a random image to check how it looks

orig_size = 1024
new_size = 224

filename = X_train_bb[rand_image]
# img_rgb = Image.fromarray(filename, "RGB")

fig, ax = plt.subplots(1,figsize=(6,6))

# fig, a = plt.subplots(1,1)
# fig.set_size_inches(5,5)

dim = (new_size, new_size)
img_resize = cv2.resize(filename, dim, interpolation=cv2.INTER_CUBIC)

# a.imshow(img_resize, cmap=plt.cm.bone)
  
# unscaled = cv2.imread(rgb)
region = y_train_bb[rand_image]
image_height, image_width, _ = filename.shape

print("region",region)

print(image_height)
print(image_width)

x0 = ((region[0] * new_size ) / orig_size)
y0 = ((region[1] * new_size ) / orig_size)
x1 = ((region[2] * new_size ) / orig_size)
y1 = ((region[3] * new_size ) / orig_size)

# x0 = int(region[0] * image_width / IMAGE_SIZE) # Scale the BBox
# y0 = int(region[1] * image_height / IMAGE_SIZE)

# x1 = int((region[0] + region[2]) * image_width / IMAGE_SIZE)
# y1 = int((region[1] + region[3]) * image_height / IMAGE_SIZE)

# Create a Rectangle patch
rect = patches.Rectangle((x0, y0), x1, y1, linewidth=2, edgecolor='r', facecolor='none')
# rect.append(patches.Rectangle((X, Y), width, height,linewidth = 3,edgecolor = 'r',facecolor = 'none'))

print(rect)

# Add the patch to the Axes
ax.imshow(img_resize, cmap=plt.cm.bone)
ax.add_patch(rect)

# ax.set_xlim(-3,3)
# ax.set_ylim(-3,3)

plt.show()
plt.figure().clear()

region [513. 472. 320. 342.]
224
224
Rectangle(xy=(112.219, 103.25), width=70, height=74.8125, angle=0)


print("X_train_bb",X_train_bb.shape)
print("y_train_bb",y_train_bb.shape)

X_train_bb (4500, 224, 224, 3)
y_train_bb (4500, 4)


X_train, X_test, y_train, y_test = train_test_split(X_train_bb, y_train_bb, test_size=.20, random_state=10)  # 80% Training and 20% Testing


print("X_train shape",X_train.shape)
print("y train shape",y_train.shape)
print("x_test shape",X_test.shape)
print("y_test shape",y_test.shape)

X_train shape (3600, 224, 224, 3)
y train shape (3600, 4)
x_test shape (900, 224, 224, 3)
y_test shape (900, 4)


X_train1, X_val, y_train1, y_val = train_test_split(X_train, y_train, test_size=.20, random_state=20)  # 80% Training and 20% Testing


print("X_train1 shape",X_train1.shape)
print("y train1 shape",y_train1.shape)
print("X_val shape",X_val.shape)
print("y_val shape",y_val.shape)

X_train1 shape (2880, 224, 224, 3)
y train1 shape (2880, 4)
X_val shape (720, 224, 224, 3)
y_val shape (720, 4)


ALPHA = 1.0 # Width hyper parameter for MobileNet (0.25, 0.5, 0.75, 1.0). Higher width means more accurate but slower

from tensorflow.keras.layers import Conv2D, Reshape

def create_model_2(trainable=True):
    model = MobileNet(input_shape=(128, 128, 3), include_top=False, alpha=ALPHA) # Load pre-trained mobilenet
    # Do not include classification (top) layer

    # to freeze layers, except the new top layer, of course, which will be added below
    for layer in model.layers:
        layer.trainable = trainable

    # Add new top layer which is a conv layer of the same size as the previous layer so that only 4 coords of BBox can be output
    x0 = model.layers[-1].output
    # x1 = Conv2D(4, kernel_size=4, kernel_initializer='normal', kernel_regularizer=tf.keras.regularizers.l2(0.01), activity_regularizer=tf.keras.regularizers.l1(0.001), activation="relu", name="coords")(x0)
    x1 = Conv2D(4, kernel_size=4, kernel_initializer='normal', kernel_regularizer=tf.keras.regularizers.l2(0.0015), activity_regularizer=tf.keras.regularizers.l1(0.0025), activation="relu", name="coords")(x0)
    # x1 = Conv2D(4, kernel_size=4, name="coords")(x0)
    # x1 = Conv2D(4, kernel_size=4, kernel_initializer='he_normal', activation="relu", name="coords1")(x1)
    # x1 = Conv2D(4, kernel_size=4, kernel_initializer='he_normal', activation="relu", name="coords2")(x1)
    # In the line above kernel size should be 3 for img size 96, 4 for img size 128, 5 for img size 160 etc.
    x2 = Reshape((4,))(x1) # These are the 4 predicted coordinates of one BBox

    return Model(inputs=model.input, outputs=x2)


def IOU_1(y_true, y_pred):
    intersections = 0
    unions = 0
    # set the types so we are sure what type we are using

    gt = y_true
    pred = y_pred
    # Compute interection of predicted (pred) and ground truth (gt) bounding boxes
    diff_width = np.minimum(gt[:,0] + gt[:,2], pred[:,0] + pred[:,2]) - np.maximum(gt[:,0], pred[:,0])
    diff_height = np.minimum(gt[:,1] + gt[:,3], pred[:,1] + pred[:,3]) - np.maximum(gt[:,1], pred[:,1])
    intersection = diff_width * diff_height

    # Compute union
    area_gt = gt[:,2] * gt[:,3]
    area_pred = pred[:,2] * pred[:,3]
    union = area_gt + area_pred - intersection

    # Compute intersection and union over multiple boxes
    for j, _ in enumerate(union):
        if union[j] > 0 and intersection[j] > 0 and union[j] >= intersection[j]:
            intersections += intersection[j]
            unions += union[j]

    # Compute IOU. Use epsilon to prevent division by zero
    iou = np.round(intersections / (unions + tf.keras.backend.epsilon()), 4)
    # This must match the type used in py_func
    iou = iou.astype(np.float32)
    return iou


def IoU_2(y_true, y_pred):
    iou = tf.py_function(IOU_1, [y_true, y_pred], Tout=tf.float32)
    return iou


base_model1 = create_model_2(False)


base_model1.summary()

Model: "model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_1 (InputLayer)        [(None, 128, 128, 3)]     0         
                                                                 
 conv1 (Conv2D)              (None, 64, 64, 32)        864       
                                                                 
 conv1_bn (BatchNormalizatio  (None, 64, 64, 32)       128       
 n)                                                              
                                                                 
 conv1_relu (ReLU)           (None, 64, 64, 32)        0         
                                                                 
 conv_dw_1 (DepthwiseConv2D)  (None, 64, 64, 32)       288       
                                                                 
 conv_dw_1_bn (BatchNormaliz  (None, 64, 64, 32)       128       
 ation)                                                          
                                                                 
 conv_dw_1_relu (ReLU)       (None, 64, 64, 32)        0         
                                                                 
 conv_pw_1 (Conv2D)          (None, 64, 64, 64)        2048      
                                                                 
 conv_pw_1_bn (BatchNormaliz  (None, 64, 64, 64)       256       
 ation)                                                          
                                                                 
 conv_pw_1_relu (ReLU)       (None, 64, 64, 64)        0         
                                                                 
 conv_pad_2 (ZeroPadding2D)  (None, 65, 65, 64)        0         
                                                                 
 conv_dw_2 (DepthwiseConv2D)  (None, 32, 32, 64)       576       
                                                                 
 conv_dw_2_bn (BatchNormaliz  (None, 32, 32, 64)       256       
 ation)                                                          
                                                                 
 conv_dw_2_relu (ReLU)       (None, 32, 32, 64)        0         
                                                                 
 conv_pw_2 (Conv2D)          (None, 32, 32, 128)       8192      
                                                                 
 conv_pw_2_bn (BatchNormaliz  (None, 32, 32, 128)      512       
 ation)                                                          
                                                                 
 conv_pw_2_relu (ReLU)       (None, 32, 32, 128)       0         
                                                                 
 conv_dw_3 (DepthwiseConv2D)  (None, 32, 32, 128)      1152      
                                                                 
 conv_dw_3_bn (BatchNormaliz  (None, 32, 32, 128)      512       
 ation)                                                          
                                                                 
 conv_dw_3_relu (ReLU)       (None, 32, 32, 128)       0         
                                                                 
 conv_pw_3 (Conv2D)          (None, 32, 32, 128)       16384     
                                                                 
 conv_pw_3_bn (BatchNormaliz  (None, 32, 32, 128)      512       
 ation)                                                          
                                                                 
 conv_pw_3_relu (ReLU)       (None, 32, 32, 128)       0         
                                                                 
 conv_pad_4 (ZeroPadding2D)  (None, 33, 33, 128)       0         
                                                                 
 conv_dw_4 (DepthwiseConv2D)  (None, 16, 16, 128)      1152      
                                                                 
 conv_dw_4_bn (BatchNormaliz  (None, 16, 16, 128)      512       
 ation)                                                          
                                                                 
 conv_dw_4_relu (ReLU)       (None, 16, 16, 128)       0         
                                                                 
 conv_pw_4 (Conv2D)          (None, 16, 16, 256)       32768     
                                                                 
 conv_pw_4_bn (BatchNormaliz  (None, 16, 16, 256)      1024      
 ation)                                                          
                                                                 
 conv_pw_4_relu (ReLU)       (None, 16, 16, 256)       0         
                                                                 
 conv_dw_5 (DepthwiseConv2D)  (None, 16, 16, 256)      2304      
                                                                 
 conv_dw_5_bn (BatchNormaliz  (None, 16, 16, 256)      1024      
 ation)                                                          
                                                                 
 conv_dw_5_relu (ReLU)       (None, 16, 16, 256)       0         
                                                                 
 conv_pw_5 (Conv2D)          (None, 16, 16, 256)       65536     
                                                                 
 conv_pw_5_bn (BatchNormaliz  (None, 16, 16, 256)      1024      
 ation)                                                          
                                                                 
 conv_pw_5_relu (ReLU)       (None, 16, 16, 256)       0         
                                                                 
 conv_pad_6 (ZeroPadding2D)  (None, 17, 17, 256)       0         
                                                                 
 conv_dw_6 (DepthwiseConv2D)  (None, 8, 8, 256)        2304      
                                                                 
 conv_dw_6_bn (BatchNormaliz  (None, 8, 8, 256)        1024      
 ation)                                                          
                                                                 
 conv_dw_6_relu (ReLU)       (None, 8, 8, 256)         0         
                                                                 
 conv_pw_6 (Conv2D)          (None, 8, 8, 512)         131072    
                                                                 
 conv_pw_6_bn (BatchNormaliz  (None, 8, 8, 512)        2048      
 ation)                                                          
                                                                 
 conv_pw_6_relu (ReLU)       (None, 8, 8, 512)         0         
                                                                 
 conv_dw_7 (DepthwiseConv2D)  (None, 8, 8, 512)        4608      
                                                                 
 conv_dw_7_bn (BatchNormaliz  (None, 8, 8, 512)        2048      
 ation)                                                          
                                                                 
 conv_dw_7_relu (ReLU)       (None, 8, 8, 512)         0         
                                                                 
 conv_pw_7 (Conv2D)          (None, 8, 8, 512)         262144    
                                                                 
 conv_pw_7_bn (BatchNormaliz  (None, 8, 8, 512)        2048      
 ation)                                                          
                                                                 
 conv_pw_7_relu (ReLU)       (None, 8, 8, 512)         0         
                                                                 
 conv_dw_8 (DepthwiseConv2D)  (None, 8, 8, 512)        4608      
                                                                 
 conv_dw_8_bn (BatchNormaliz  (None, 8, 8, 512)        2048      
 ation)                                                          
                                                                 
 conv_dw_8_relu (ReLU)       (None, 8, 8, 512)         0         
                                                                 
 conv_pw_8 (Conv2D)          (None, 8, 8, 512)         262144    
                                                                 
 conv_pw_8_bn (BatchNormaliz  (None, 8, 8, 512)        2048      
 ation)                                                          
                                                                 
 conv_pw_8_relu (ReLU)       (None, 8, 8, 512)         0         
                                                                 
 conv_dw_9 (DepthwiseConv2D)  (None, 8, 8, 512)        4608      
                                                                 
 conv_dw_9_bn (BatchNormaliz  (None, 8, 8, 512)        2048      
 ation)                                                          
                                                                 
 conv_dw_9_relu (ReLU)       (None, 8, 8, 512)         0         
                                                                 
 conv_pw_9 (Conv2D)          (None, 8, 8, 512)         262144    
                                                                 
 conv_pw_9_bn (BatchNormaliz  (None, 8, 8, 512)        2048      
 ation)                                                          
                                                                 
 conv_pw_9_relu (ReLU)       (None, 8, 8, 512)         0         
                                                                 
 conv_dw_10 (DepthwiseConv2D  (None, 8, 8, 512)        4608      
 )                                                               
                                                                 
 conv_dw_10_bn (BatchNormali  (None, 8, 8, 512)        2048      
 zation)                                                         
                                                                 
 conv_dw_10_relu (ReLU)      (None, 8, 8, 512)         0         
                                                                 
 conv_pw_10 (Conv2D)         (None, 8, 8, 512)         262144    
                                                                 
 conv_pw_10_bn (BatchNormali  (None, 8, 8, 512)        2048      
 zation)                                                         
                                                                 
 conv_pw_10_relu (ReLU)      (None, 8, 8, 512)         0         
                                                                 
 conv_dw_11 (DepthwiseConv2D  (None, 8, 8, 512)        4608      
 )                                                               
                                                                 
 conv_dw_11_bn (BatchNormali  (None, 8, 8, 512)        2048      
 zation)                                                         
                                                                 
 conv_dw_11_relu (ReLU)      (None, 8, 8, 512)         0         
                                                                 
 conv_pw_11 (Conv2D)         (None, 8, 8, 512)         262144    
                                                                 
 conv_pw_11_bn (BatchNormali  (None, 8, 8, 512)        2048      
 zation)                                                         
                                                                 
 conv_pw_11_relu (ReLU)      (None, 8, 8, 512)         0         
                                                                 
 conv_pad_12 (ZeroPadding2D)  (None, 9, 9, 512)        0         
                                                                 
 conv_dw_12 (DepthwiseConv2D  (None, 4, 4, 512)        4608      
 )                                                               
                                                                 
 conv_dw_12_bn (BatchNormali  (None, 4, 4, 512)        2048      
 zation)                                                         
                                                                 
 conv_dw_12_relu (ReLU)      (None, 4, 4, 512)         0         
                                                                 
 conv_pw_12 (Conv2D)         (None, 4, 4, 1024)        524288    
                                                                 
 conv_pw_12_bn (BatchNormali  (None, 4, 4, 1024)       4096      
 zation)                                                         
                                                                 
 conv_pw_12_relu (ReLU)      (None, 4, 4, 1024)        0         
                                                                 
 conv_dw_13 (DepthwiseConv2D  (None, 4, 4, 1024)       9216      
 )                                                               
                                                                 
 conv_dw_13_bn (BatchNormali  (None, 4, 4, 1024)       4096      
 zation)                                                         
                                                                 
 conv_dw_13_relu (ReLU)      (None, 4, 4, 1024)        0         
                                                                 
 conv_pw_13 (Conv2D)         (None, 4, 4, 1024)        1048576   
                                                                 
 conv_pw_13_bn (BatchNormali  (None, 4, 4, 1024)       4096      
 zation)                                                         
                                                                 
 conv_pw_13_relu (ReLU)      (None, 4, 4, 1024)        0         
                                                                 
 coords (Conv2D)             (None, 1, 1, 4)           65540     
                                                                 
 reshape (Reshape)           (None, 4)                 0         
                                                                 
=================================================================
Total params: 3,294,404
Trainable params: 65,540
Non-trainable params: 3,228,864
_________________________________________________________________


X_train1.shape[0]

2880


X_train2 = np.resize(X_train1,(X_train1.shape[0],128,128,3))


X_train1.shape, X_train2.shape

((2880, 224, 224, 3), (2880, 128, 128, 3))


X_val.shape

(720, 224, 224, 3)


X_val2 = np.resize(X_val,(X_val.shape[0],128,128,3))


X_val.shape,X_val2.shape

((720, 224, 224, 3), (720, 128, 128, 3))


X_test2 = np.resize(X_test,(X_test.shape[0],128,128,3))


X_test.shape, X_test2.shape

((900, 224, 224, 3), (900, 128, 128, 3))


print(X_train2.shape)
print(X_val2.shape)
print(X_test2.shape)

(2880, 128, 128, 3)
(720, 128, 128, 3)
(900, 128, 128, 3)


y_train1[0:5]

array([[310., 607., 239., 259.],
       [568., 481., 194., 173.],
       [273., 209., 194., 393.],
       [217., 526., 176., 125.],
       [558., 280., 243., 482.]])


y_train2 = (y_train1 * 128) / 1024


y_train2[0:5]

array([[38.75 , 75.875, 29.875, 32.375],
       [71.   , 60.125, 24.25 , 21.625],
       [34.125, 26.125, 24.25 , 49.125],
       [27.125, 65.75 , 22.   , 15.625],
       [69.75 , 35.   , 30.375, 60.25 ]])


y_val2 = (y_val * 128 )/ 1024


y_val2[0:5]

array([[19.25 , 34.875, 41.75 , 50.375],
       [42.125, 58.125, 23.5  , 23.5  ],
       [78.25 , 24.625, 43.   , 83.   ],
       [75.375, 35.25 , 27.375, 31.625],
       [91.25 , 70.   , 13.   , 28.875]])


y_test2 = (y_test * 128 )/ 1024


# Compile the model
opt = optimizer=Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
base_model1.compile(loss="mean_squared_error", optimizer=opt, metrics=[IoU_2]) # Regression loss is MSE


# Use earlystopping
callback2 = tf.keras.callbacks.EarlyStopping(monitor='val_IoU_2', patience=3, min_delta=0.01)

# Fit the model
base_model1.fit(X_train2, y_train2, validation_data=(X_val2, y_val2), epochs=30, batch_size=20, callbacks=[callback2])

Epoch 1/30
144/144 [==============================] - 20s 127ms/step - loss: 1828.7288 - IoU_2: 0.3005 - val_loss: 1778.9187 - val_IoU_2: 0.2710
Epoch 2/30
144/144 [==============================] - 18s 127ms/step - loss: 1507.3427 - IoU_2: 0.2646 - val_loss: 1301.6140 - val_IoU_2: 0.2105
Epoch 3/30
144/144 [==============================] - 18s 125ms/step - loss: 1015.9293 - IoU_2: 0.1621 - val_loss: 890.5369 - val_IoU_2: 0.1570
Epoch 4/30
144/144 [==============================] - 18s 127ms/step - loss: 729.0668 - IoU_2: 0.1625 - val_loss: 679.5276 - val_IoU_2: 0.1547
Epoch 5/30
144/144 [==============================] - 18s 128ms/step - loss: 575.9411 - IoU_2: 0.1702 - val_loss: 559.8184 - val_IoU_2: 0.1565
Epoch 6/30
144/144 [==============================] - 19s 131ms/step - loss: 491.3201 - IoU_2: 0.1630 - val_loss: 492.2207 - val_IoU_2: 0.1503

<keras.callbacks.History at 0x27ca0111730>


base_model1.evaluate(X_test2, y_test2)

29/29 [==============================] - 5s 164ms/step - loss: 461.3999 - IoU_2: 0.1692

[461.3999328613281, 0.16920344531536102]


pkl.dump(base_model1, open('mobilenet_objdet', 'wb'))

WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 28). These functions will not be directly callable after loading.

INFO:tensorflow:Assets written to: ram://665fd77b-c745-41cd-8d15-93c098395e0a/assets

INFO:tensorflow:Assets written to: ram://665fd77b-c745-41cd-8d15-93c098395e0a/assets


X_train4 = X_train2/255.0

X_val4 = X_val2/255.0

X_test4 = X_test2/255.0


tf.keras.backend.clear_session()

# Use earlystopping
callback2 = tf.keras.callbacks.EarlyStopping(monitor='val_IoU_2', patience=3, min_delta=0.01)

# Fit the model
base_model1.fit(X_train4, y_train2, validation_data=(X_val4, y_val2), epochs=30, batch_size=20, callbacks=[callback2])

Epoch 1/30
144/144 [==============================] - 20s 130ms/step - loss: 431.2441 - IoU_2: 0.1555 - val_loss: 431.0265 - val_IoU_2: 0.1440
Epoch 2/30
144/144 [==============================] - 16s 112ms/step - loss: 399.6055 - IoU_2: 0.1331 - val_loss: 412.4891 - val_IoU_2: 0.1263
Epoch 3/30
144/144 [==============================] - 16s 112ms/step - loss: 390.5389 - IoU_2: 0.1243 - val_loss: 405.8780 - val_IoU_2: 0.1212
Epoch 4/30
144/144 [==============================] - 16s 110ms/step - loss: 387.4046 - IoU_2: 0.1203 - val_loss: 402.8160 - val_IoU_2: 0.1211
Epoch 5/30
144/144 [==============================] - 16s 110ms/step - loss: 385.9109 - IoU_2: 0.1181 - val_loss: 401.5852 - val_IoU_2: 0.1194

<keras.callbacks.History at 0x282a356ac70>


train_4500_rows.sample(10).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


samp_data = train_4500_rows["patientId"].sample(10)


sample_data2 = ['00436515-870c-4b36-a041-de91049b9ab4',
                '00704310-78a8-4b38-8475-49f4573b2dbb',
                '00aecb01-a116-45a2-956c-08d2fa55433f',
                '00c0b293-48e7-4e16-ac76-9269ba535a62',
                '00f08de1-517e-4652-a04f-d1dc9ee48593']


print(sample_data2)

['00436515-870c-4b36-a041-de91049b9ab4', '00704310-78a8-4b38-8475-49f4573b2dbb', '00aecb01-a116-45a2-956c-08d2fa55433f', '00c0b293-48e7-4e16-ac76-9269ba535a62', '00f08de1-517e-4652-a04f-d1dc9ee48593']


train_coords = train_4500_rows[["patientId", "x", "y", "width", "height"]]


train_coords.sample(10).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


train_coords[train_coords["patientId"].isin(sample_data2)].style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


train_coords.dtypes

patientId     object
x            float64
y            float64
width        float64
height       float64
dtype: object


def gt_pb(pass_model,data):
    passed_sample = data
    for loop_1 in passed_sample:
        pat_id_1 = loop_1

        no_ind = train_coords[train_coords['patientId'] == pat_id_1].index.values
        l_ind = len(no_ind)

        fname = pat_id_1+".dcm"
        image_path = "stage_2_train_images/"+fname

        ds = dicom.dcmread(image_path)
        img = ds.pixel_array

        print("patientid",pat_id_1)

        fig, a = plt.subplots(1,1)
        fig.set_size_inches(5,5)

        X = 0
        Y = 0
        width = 0
        height = 0
        rect = []
        rect1 = []
        k = 0

        for j in range(l_ind):
            X = train_coords.iloc[no_ind[j],1]
            Y = train_coords.iloc[no_ind[j],2]
            width = train_coords.iloc[no_ind[j],3]
            height = train_coords.iloc[no_ind[j],4]
            rect.append(patches.Rectangle((X, Y), width, height,linewidth = 3,edgecolor = 'r',facecolor = 'none'))
            a.imshow(img, cmap=plt.cm.bone)
            a.add_patch(rect[j])
        
        for k in range(l_ind):
            img_for_prediction =img[tf.newaxis,...]
            img_pred_128 = np.resize(img,(img_for_prediction.shape[0],128,128,3))
            img_pred_region = pass_model.predict(img_pred_128) # Predict the BBox
            x0 = (img_pred_region[0,0])
            y0 = (img_pred_region[0,1])
            x1 = (img_pred_region[0,2])
            y1 = (img_pred_region[0,3]) 
            rect1.append(patches.Rectangle((x0*8, y0*8), x1*8, y1*8, linewidth=3, edgecolor='y', facecolor='none'))  
            a.add_patch(rect1[k])
        plt.show()
        plt.figure().clear()


gt_pb(base_model1, sample_data2)

patientid 00436515-870c-4b36-a041-de91049b9ab4
1/1 [==============================] - 0s 431ms/step
1/1 [==============================] - 0s 64ms/step

patientid 00704310-78a8-4b38-8475-49f4573b2dbb
1/1 [==============================] - 0s 75ms/step
1/1 [==============================] - 0s 65ms/step

<Figure size 640x480 with 0 Axes>

patientid 00aecb01-a116-45a2-956c-08d2fa55433f
1/1 [==============================] - 0s 72ms/step
1/1 [==============================] - 0s 64ms/step

<Figure size 640x480 with 0 Axes>

patientid 00c0b293-48e7-4e16-ac76-9269ba535a62
1/1 [==============================] - 0s 68ms/step
1/1 [==============================] - 0s 63ms/step

<Figure size 640x480 with 0 Axes>

patientid 00f08de1-517e-4652-a04f-d1dc9ee48593
1/1 [==============================] - 0s 63ms/step
1/1 [==============================] - 0s 62ms/step

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>


samp_data_arr = np.array(samp_data)


samp_data_arr

array(['47cb6d42-b61b-4cdc-b865-b26f2196f979',
       '729f2aa0-9564-4228-b516-1d8d4be8bb55',
       '78c9b88e-a134-4470-b161-22e4a698206c',
       '2b70a757-4dff-4d97-baf5-f41b8d3ab4da',
       '88b5a0c7-4f3b-4a3a-9882-617c1b87c187',
       '05fe7d73-5c8c-4349-88c4-2071a00d6d81',
       '15cfe136-58ea-4e38-b3a8-d372ecad4ac7',
       '3162d754-7972-47b0-ae60-1ae6e2bb4150',
       '7bb1c647-d8d1-4640-b495-f41c4d2a34ca',
       '81d9d146-e9bb-478b-aeed-c2431780030e'], dtype=object)


gt_pb(base_model1, samp_data_arr)

patientid 47cb6d42-b61b-4cdc-b865-b26f2196f979
1/1 [==============================] - 0s 68ms/step
1/1 [==============================] - 0s 67ms/step

patientid 729f2aa0-9564-4228-b516-1d8d4be8bb55
1/1 [==============================] - 0s 67ms/step

<Figure size 640x480 with 0 Axes>

patientid 78c9b88e-a134-4470-b161-22e4a698206c
1/1 [==============================] - 0s 71ms/step
1/1 [==============================] - 0s 65ms/step
1/1 [==============================] - 0s 61ms/step

<Figure size 640x480 with 0 Axes>

patientid 2b70a757-4dff-4d97-baf5-f41b8d3ab4da
1/1 [==============================] - 0s 84ms/step
1/1 [==============================] - 0s 65ms/step

<Figure size 640x480 with 0 Axes>

patientid 88b5a0c7-4f3b-4a3a-9882-617c1b87c187
1/1 [==============================] - 0s 71ms/step
1/1 [==============================] - 0s 68ms/step

<Figure size 640x480 with 0 Axes>

patientid 05fe7d73-5c8c-4349-88c4-2071a00d6d81
1/1 [==============================] - 0s 76ms/step
1/1 [==============================] - 0s 76ms/step


# path of pretrained models weights
weights_path = "brucechou1983_CheXNet_Keras_0.3.0_weights.h5"


# from tensorflow.keras.applications.densenet.DenseNet import Densenet121
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, Reshape

def create_model_new(trainable=True):
    model = tf.keras.applications.DenseNet121(weights=None,include_top=False,input_shape=(128,128,3))
    model.load_weights(weights_path,by_name= True)

    # to freeze layers, except the new top layer, of course, which will be added below
    for layer in model.layers:
        layer.trainable = trainable

    # Add new top layer which is a conv layer of the same size as the previous layer so that only 4 coords of BBox can be output
    x0 = model.layers[-1].output
    x1 = Conv2D(4, kernel_size=4, kernel_initializer='normal', kernel_regularizer=tf.keras.regularizers.l2(0.0015), 
                     activity_regularizer=tf.keras.regularizers.l1(0.0025), activation="relu", name="coords")(x0)
    x2 = Reshape((4,))(x1) # These are the 4 predicted coordinates of one BBox

    return Model(inputs=model.input, outputs=x2)


base_model2 = create_model_new(False)


# Compile the model

opt = Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
          
base_model2.compile(loss="mean_squared_error", optimizer=opt, metrics=[IoU_2]) # Regression loss is MSE


# Use earlystopping
callback2 = tf.keras.callbacks.EarlyStopping(monitor='val_IoU_2', patience=5, min_delta=0.01)

# Fit the model
base_model2.fit(X_train2, y_train2, validation_data=(X_val2, y_val2), epochs=40, batch_size=20, callbacks=[callback2])

Epoch 1/40
144/144 [==============================] - 500s 3s/step - loss: 2006.0892 - IoU_2: 0.1283 - val_loss: 845.5762 - val_IoU_2: 0.1521
Epoch 2/40
144/144 [==============================] - 476s 3s/step - loss: 757.3915 - IoU_2: 0.1580 - val_loss: 740.6445 - val_IoU_2: 0.1551
Epoch 3/40
144/144 [==============================] - 467s 3s/step - loss: 699.4479 - IoU_2: 0.1572 - val_loss: 704.5209 - val_IoU_2: 0.1522
Epoch 4/40
144/144 [==============================] - 462s 3s/step - loss: 667.6571 - IoU_2: 0.1515 - val_loss: 682.9375 - val_IoU_2: 0.1499
Epoch 5/40
144/144 [==============================] - 466s 3s/step - loss: 645.8419 - IoU_2: 0.1494 - val_loss: 666.6833 - val_IoU_2: 0.1383
Epoch 6/40
144/144 [==============================] - 392s 3s/step - loss: 630.7343 - IoU_2: 0.1424 - val_loss: 647.8849 - val_IoU_2: 0.1496
Epoch 7/40
144/144 [==============================] - 284s 2s/step - loss: 618.7048 - IoU_2: 0.1416 - val_loss: 648.3870 - val_IoU_2: 0.1419
Epoch 8/40
144/144 [==============================] - 281s 2s/step - loss: 608.3607 - IoU_2: 0.1426 - val_loss: 655.6977 - val_IoU_2: 0.1450
Epoch 9/40
144/144 [==============================] - 282s 2s/step - loss: 601.9389 - IoU_2: 0.1427 - val_loss: 637.2079 - val_IoU_2: 0.1454
Epoch 10/40
144/144 [==============================] - 281s 2s/step - loss: 594.9384 - IoU_2: 0.1425 - val_loss: 622.7687 - val_IoU_2: 0.1504

<keras.callbacks.History at 0x1d834336b50>


base_model2.evaluate(X_test2, y_test2)

29/29 [==============================] - 58s 2s/step - loss: 598.5198 - IoU_2: 0.1362

[598.519775390625, 0.1362275779247284]


y_pred = base_model2.predict(X_test2)

29/29 [==============================] - 57s 2s/step


pkl.dump(base_model2, open('densenet_objdet', 'wb'))

WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 121). These functions will not be directly callable after loading.

INFO:tensorflow:Assets written to: ram://2703cdd6-e8d6-4f40-b48b-6cfbe5a12d57/assets

INFO:tensorflow:Assets written to: ram://2703cdd6-e8d6-4f40-b48b-6cfbe5a12d57/assets


print(y_test2[1], y_pred[1],'\n')
print(y_test2[11], y_pred[11],'\n')
print(y_test2[111], y_pred[111],'\n')
print(y_test2[21], y_pred[21],'\n')

[75.75  39.    25.125 60.   ] [45.190815  48.243572   7.9350333 55.637497 ] 

[69.625 27.125 12.375 18.75 ] [54.278156 52.235233  0.       41.434162] 

[34.5   55.    33.875 60.125] [46.794376 39.954098  0.       50.356937] 

[68.25  24.625 32.25  54.875] [16.15029  52.620552 32.511173 29.475729]


gt_pb(base_model2, sample_data2)

patientid 00436515-870c-4b36-a041-de91049b9ab4
1/1 [==============================] - 1s 942ms/step
1/1 [==============================] - 1s 843ms/step

patientid 00704310-78a8-4b38-8475-49f4573b2dbb
1/1 [==============================] - 1s 869ms/step
1/1 [==============================] - 1s 764ms/step

<Figure size 640x480 with 0 Axes>

patientid 00aecb01-a116-45a2-956c-08d2fa55433f
1/1 [==============================] - 1s 772ms/step
1/1 [==============================] - 1s 857ms/step

<Figure size 640x480 with 0 Axes>

patientid 00c0b293-48e7-4e16-ac76-9269ba535a62
1/1 [==============================] - 1s 833ms/step
1/1 [==============================] - 1s 953ms/step

<Figure size 640x480 with 0 Axes>

patientid 00f08de1-517e-4652-a04f-d1dc9ee48593
1/1 [==============================] - 1s 892ms/step
1/1 [==============================] - 1s 766ms/step

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>


gt_pb(base_model2, samp_data_arr)

patientid 47cb6d42-b61b-4cdc-b865-b26f2196f979
1/1 [==============================] - 1s 823ms/step
1/1 [==============================] - 1s 740ms/step

patientid 729f2aa0-9564-4228-b516-1d8d4be8bb55
1/1 [==============================] - 1s 963ms/step

<Figure size 640x480 with 0 Axes>

patientid 78c9b88e-a134-4470-b161-22e4a698206c
1/1 [==============================] - 1s 807ms/step
1/1 [==============================] - 1s 892ms/step
1/1 [==============================] - 1s 788ms/step

<Figure size 640x480 with 0 Axes>

patientid 2b70a757-4dff-4d97-baf5-f41b8d3ab4da
1/1 [==============================] - 1s 819ms/step
1/1 [==============================] - 1s 824ms/step

<Figure size 640x480 with 0 Axes>

patientid 88b5a0c7-4f3b-4a3a-9882-617c1b87c187
1/1 [==============================] - 1s 858ms/step
1/1 [==============================] - 1s 878ms/step

<Figure size 640x480 with 0 Axes>

patientid 05fe7d73-5c8c-4349-88c4-2071a00d6d81
1/1 [==============================] - 1s 778ms/step
1/1 [==============================] - 1s 809ms/step


!pip install pycocotools

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!git clone --depth 1 https://github.com/tensorflow/models

fatal: destination path 'models' already exists and is not an empty directory.


%%cmd
cd models/research/
protoc object_detection/protos/*.proto --python_out=.

Microsoft Windows [Version 10.0.22621.819]
(c) Microsoft Corporation. All rights reserved.

C:\Users\thril>cd models/research/

C:\Users\thril\models\research>protoc object_detection/protos/*.proto --python_out=.

C:\Users\thril\models\research>


!cd models


os.chdir("models/research")


%%cmd
cd models/research/
protoc object_detection/protos/*.proto --python_out=.
cp object_detection/packages/tf2/setup.py .
python -m pip install .

Microsoft Windows [Version 10.0.22621.819]
(c) Microsoft Corporation. All rights reserved.

C:\Users\thril\models\research>cd models/research/

C:\Users\thril\models\research>protoc object_detection/protos/*.proto --python_out=.

C:\Users\thril\models\research>cp object_detection/packages/tf2/setup.py .

C:\Users\thril\models\research>python -m pip install .
Processing c:\users\thril\models\research
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Building wheels for collected packages: object-detection
  Building wheel for object-detection (setup.py): started
  Building wheel for object-detection (setup.py): finished with status 'done'
  Created wheel for object-detection: filename=object_detection-0.1-py3-none-any.whl size=1666274 sha256=dd41be69fbe91af582f21371b566cc0a8ca38fda7d58dba061029b5fcb433f9a
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Successfully built object-detection
Installing collected packages: protobuf, orjson, opencv-python-headless, opencv-python, objsize, importlib_resources, immutabledict, httplib2, fasteners, fastavro, etils, dill, cloudpickle, avro-python3, tensorflow-addons, sacrebleu, proto-plus, oauth2client, kaggle, hdfs, googleapis-common-protos, tensorflow-metadata, seqeval, lvis, google-auth-httplib2, google-api-core, apache-beam, tensorflow-datasets, google-api-python-client, tensorflow-text, tf-models-official, object-detection
  Attempting uninstall: protobuf
    Found existing installation: protobuf 3.20.1
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      Successfully uninstalled protobuf-3.20.1
  Rolling back uninstall of protobuf
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   from C:\Users\thril\anaconda3\Lib\site-packages\~rotobuf-3.20.1-py3.9.egg-info

C:\Users\thril\models\research>

The system cannot find the path specified.
ERROR: Could not install packages due to an OSError: [WinError 5] Access is denied: 'C:\\Users\\thril\\anaconda3\\Lib\\site-packages\\google\\protobuf\\internal\\_api_implementation.cp39-win_amd64.pyd'
Consider using the `--user` option or check the permissions.


#pwd
#Run !cd.. 2 times after importing the models from the above code.
#This will point the project to the earlier project execution directory
#Otherwise we cannot execute the commands as the working directory is changed earlier
!cd..


# patch tf1 into `utils.ops`
utils_ops.tensorflow = tf.compat.v1

# Patch the location of gfile
tf.gfile = tf.io.gfile


import tarfile

with tarfile.open('faster_rcnn_nas_coco_2018_01_28.tar.gz') as tgf:
    tgf.extractall()
    tgf.close()


def load_model(model_name):

    model_dir = pathlib.Path(model_name)/"saved_model"
    model = tf.saved_model.load(str(model_dir))
    model = model.signatures['serving_default']
    
    return model


model_name = 'faster_rcnn_nas_coco_2018_01_28'
detection_model = load_model(model_name)

INFO:tensorflow:Saver not created because there are no variables in the graph to restore


# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = "labels.pbtxt"
print(PATH_TO_LABELS)
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)

labels.pbtxt


detection_model.inputs

[<tf.Tensor 'image_tensor:0' shape=(None, None, None, 3) dtype=uint8>]


detection_model.output_shapes

{'detection_scores': TensorShape([None, 100]),
 'num_detections': TensorShape([None]),
 'detection_classes': TensorShape([None, 100]),
 'detection_boxes': TensorShape([None, 100, 4])}


def run_inference_for_single_image(model, image):
    image = np.asarray(image)
    rgb = np.stack([image]*3,axis=2) # to convert into RGB
    input_tensor = tf.convert_to_tensor(rgb)
    input_tensor_new = input_tensor[tf.newaxis,...]

    # Run inference
    output_dict = detection_model(input_tensor_new)
    num_detections = int(output_dict.pop('num_detections'))
    output_dict = {key:value[0, :num_detections].numpy()
                   for key,value in output_dict.items()}
    output_dict['num_detections'] = num_detections

    # detection_classes should be ints.
    output_dict['detection_classes'] = output_dict['detection_classes'].astype(np.int64)

    print("output_dict[detection_classes]",output_dict['detection_classes'])

    # Handle models with masks:
    if 'detection_masks' in output_dict:
        print("inside if statement")
        # Reframe the the bbox mask to the image size.
        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(output_dict['detection_masks'], 
                                                                              output_dict['detection_boxes'],image.shape[0], image.shape[1])
        detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5, tf.uint8)
        output_dict['detection_masks_reframed'] = detection_masks_reframed.numpy()

    return output_dict


def show_inference(model, image_path):

    # the array based representation of the image will be used later in order to prepare the result image with boxes and labels on it.
    image_np = np.array(Image.open(image_path))

    # Actual detection.
    output_dict = run_inference_for_single_image(model, image_np)

    image_np1 = np.stack([image_np]*3,axis=2) # to convert into RGB

    # Visualization of the results of a detection.
    vis_util.visualize_boxes_and_labels_on_image_array(
        image_np1,
        output_dict['detection_boxes'],
        output_dict['detection_classes'],
        output_dict['detection_scores'],
        category_index, 
        # instance_masks=output_dict.get('detection_masks_reframed', None),
        use_normalized_coordinates=True,
        line_thickness=8)

    fig, a = plt.subplots(1,1)
    fig.set_size_inches(3,3)

    imS = cv2.resize(image_np1, (224, 224))                # Resize image
    plt.imshow(imS)
    fig.tight_layout()
    plt.show()
    plt.figure().clear()
    plt.close()


PATH_TO_TEST_IMAGES_DIR = pathlib.Path("Testing_Images")
TEST_IMAGE_PATHS = sorted(list(PATH_TO_TEST_IMAGES_DIR.glob("*.jpg")))
TEST_IMAGE_PATHS

[Path('Testing_Images/00d7c36e-3cdf-4df6-ac03-6c30cdc8e85b.jpg'),
 Path('Testing_Images/0100515c-5204-4f31-98e0-f35e4b00004a.jpg'),
 Path('Testing_Images/016fd98f-09a8-4b99-a8df-c3c26992f22e.jpg'),
 Path('Testing_Images/02743848-e50a-4faf-a3f4-a6215613a23d.jpg'),
 Path('Testing_Images/02cfa7d1-61e0-4d9e-8664-9a7469ccab76.jpg'),
 Path('Testing_Images/02de2d68-b7f9-428b-ac12-0cb8f56a0145.jpg'),
 Path('Testing_Images/02ea6b76-4189-47c5-a8bf-70590f84ebd8.jpg'),
 Path('Testing_Images/03d92597-3e33-4fdf-8db5-a27cf5b8d3eb.jpg'),
 Path('Testing_Images/03edb5ed-9e76-4abe-bc35-7bc95fea7e6a.jpg'),
 Path('Testing_Images/04558d1a-1f4d-47aa-bf07-a6f6595f9cec.jpg'),
 Path('Testing_Images/04a6aa6d-bd6d-48b1-822b-21b81c4bdf3a.jpg'),
 Path('Testing_Images/04dd9347-6215-4b34-a98f-a73e577aff9d.jpg'),
 Path('Testing_Images/04e9f692-f3d6-496b-ae0c-905137cc1f84.jpg'),
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 Path('Testing_Images/d471c394-f44a-47a4-9e41-6a651b8916cc.jpg'),
 Path('Testing_Images/d4c4c58c-bfc4-4d86-b4aa-6bec4d45a85b.jpg'),
 Path('Testing_Images/d4c52dfe-097d-433a-af41-b06df7998eb6.jpg'),
 Path('Testing_Images/d55144f1-7b8b-424a-b460-5a7051abd301.jpg'),
 Path('Testing_Images/d5523ffc-1c8a-4250-939a-3f5215397ff2.jpg'),
 Path('Testing_Images/d58c4bab-ae58-4c65-81e3-0c80bcef34c6.jpg'),
 Path('Testing_Images/d58ef1b3-d30e-45d5-bd21-3fdf7329a6aa.jpg'),
 Path('Testing_Images/d5fab21b-cd53-4a20-9702-a58ea1978623.jpg'),
 ...]


for image_path in TEST_IMAGE_PATHS[:4]:
    print(image_path)
    show_inference(detection_model, image_path)

Testing_Images\00d7c36e-3cdf-4df6-ac03-6c30cdc8e85b.jpg
output_dict[detection_classes] [ 1  1  1 65  1]

Testing_Images\0100515c-5204-4f31-98e0-f35e4b00004a.jpg
output_dict[detection_classes] [1]

Testing_Images\016fd98f-09a8-4b99-a8df-c3c26992f22e.jpg
output_dict[detection_classes] [ 1 65  1 28]

Testing_Images\02743848-e50a-4faf-a3f4-a6215613a23d.jpg
output_dict[detection_classes] [ 1 28]


# Pickling the best classification model
# Densenet121 with ChexNet Weights gave the best classification accuracy of all models at >80%
pkl.dump(new_dense_model, open('new_dense_model_2.pkl', 'wb'))


# Pickling the best object detection model
# MobileNet and DenseNet gave around same IoU, but MobileNet is much faster in execution and prediction
pkl.dump(base_model1, open('mobilenet_objdet', 'wb'))


test_submission = pd.read_csv("stage_2_sample_submission.csv")
test_submission.sample(5).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})


#Loading the Test Dicom images

items_test = get_dicom_files('stage_2_test_images/')

items_test
items_test[2]
len(items_test)

sample_test1 = 1
xray_sample_1 = items_test[sample_test1].dcmread()
xray_sample_1

dicom_test = pd.DataFrame.from_dicoms(items_test)

dicom_test
dicom_test.dtypes

SpecificCharacterSet            object
SOPClassUID                     object
SOPInstanceUID                  object
StudyDate                       object
StudyTime                       object
AccessionNumber                 object
Modality                        object
ConversionType                  object
ReferringPhysicianName          object
SeriesDescription               object
PatientName                     object
PatientID                       object
PatientBirthDate                object
PatientSex                      object
PatientAge                      object
BodyPartExamined                object
ViewPosition                    object
StudyInstanceUID                object
SeriesInstanceUID               object
StudyID                         object
SeriesNumber                     int64
InstanceNumber                   int64
PatientOrientation              object
SamplesPerPixel                  int64
PhotometricInterpretation       object
Rows                             int64
Columns                          int64
MultiPixelSpacing                int64
PixelSpacing                   float64
PixelSpacing1                  float64
BitsAllocated                    int64
BitsStored                       int64
HighBit                          int64
PixelRepresentation              int64
LossyImageCompression           object
LossyImageCompressionMethod     object
fname                           object
img_min                           int8
img_max                           int8
img_mean                       float64
img_std                        float64
img_pct_window                 float64
dtype: object


dicom_test.to_csv("dicom_test.csv", index = False)


def test_submission_csv(pass_model,data):
    passed_sample = data
    test_submission_pred = pd.DataFrame()
    for loop_1 in passed_sample:
        pat_id_1 = loop_1
        fname = pat_id_1+".dcm"
        image_path = "stage_2_test_images/"+fname
        ds = dicom.dcmread(image_path)
        img = ds.pixel_array
        img_for_prediction =img[tf.newaxis,...]
        #img_pred_128 = np.resize(img,(img_for_prediction.shape[0],128,128,3))
        img_pred_region = pass_model.predict(img_for_prediction) # Predict the BBox
        x0 = (img_pred_region[0,0])
        y0 = (img_pred_region[0,1])
        x1 = (img_pred_region[0,2])
        y1 = (img_pred_region[0,3])
        test_submission_pred = test_submission_pred.append([pat_id_1,x0,y0,x1,y1],ignore_index = True)
    test_submission.columns = ["patient_id","X","Y","Width","Height"]
    
    return test_submission.columns


dicom_test.columns

Index(['SpecificCharacterSet', 'SOPClassUID', 'SOPInstanceUID', 'StudyDate',
       'StudyTime', 'AccessionNumber', 'Modality', 'ConversionType',
       'ReferringPhysicianName', 'SeriesDescription', 'PatientName',
       'PatientID', 'PatientBirthDate', 'PatientSex', 'PatientAge',
       'BodyPartExamined', 'ViewPosition', 'StudyInstanceUID',
       'SeriesInstanceUID', 'StudyID', 'SeriesNumber', 'InstanceNumber',
       'PatientOrientation', 'SamplesPerPixel', 'PhotometricInterpretation',
       'Rows', 'Columns', 'MultiPixelSpacing', 'PixelSpacing', 'PixelSpacing1',
       'BitsAllocated', 'BitsStored', 'HighBit', 'PixelRepresentation',
       'LossyImageCompression', 'LossyImageCompressionMethod', 'fname',
       'img_min', 'img_max', 'img_mean', 'img_std', 'img_pct_window'],
      dtype='object')


dicom_test_final = pd.DataFrame(data = dicom_test['PatientID'],columns = ["PatientID"])


dicom_test_final["Image_file"] = dicom_test_final["PatientID"] + ".dcm"

dicom_test_final.sample(5)


dicom_test_final["Full_filename"] = "stage_2_test_images"+"/"+dicom_test_final["Image_file"]

dicom_test_final.sample(5).style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()


# Print images randomly from test images

def show_images(passed_value):
    show_df = dicom_test_final.sample(n=passed_value)

    if passed_value/5 == 0:
        z = math.ceil(passed_value/5)
    else:
            z = math.ceil((passed_value)/5)

    fig, ax = plt.subplots(z,5,constrained_layout=True)
    fig.set_figheight(20)
    fig.set_figwidth(20)
    
    i = 0
    j = 0

    for index, row in show_df.iterrows():
        image_path = row['Full_filename']
        ds = dicom.dcmread(image_path)
        img = ds.pixel_array

        height_1 = 224
        width_1 = 224
        dim = (width_1, height_1)
        img_resize = cv2.resize(img, dim, interpolation=cv2.INTER_LINEAR)

        ax[i][j].imshow(img_resize, cmap=plt.cm.bone)
        
        j = j+1
        if j == 5:
            i = i+1
            j = 0
    plt.figure().clear()
    plt.show()
    return(show_df)


show_images(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'})

<Figure size 640x480 with 0 Axes>


img_rows=224
img_cols=224
dim = (img_rows, img_cols)

X_target = []
brk = 0
i = 1 # initialisation
for img in tqdm(dicom_test_final["Full_filename"].values): 
    
    ds_3 = dicom.dcmread(img)
    img_3 = ds_3.pixel_array
    rgb = apply_color_lut(img_3, palette='PET')
    train_img = rgb
    try:
        train_img_resize = cv2.resize(train_img, dim, interpolation=cv2.INTER_LINEAR)
    except:
        brk +=1
        print("breaking out for",img)
        break
    height_2, width_2, layers = train_img_resize.shape
    size=(width_2,height_2)
    X_target.append(train_img_resize)
    i += 1

100%|██████████████████████████████████████████████████████████████████████████████| 3000/3000 [01:12<00:00, 41.58it/s]


fileName = "X_test_submission.pkl"
fileObject = open(fileName, 'wb')

pkl.dump(X_target, fileObject)
fileObject.close()


dicom_test_final.to_pickle("dicom_test_final.pkl") # pickling the dicom test final data


X_target_test = np.array(X_target)


def test_submission_csv(pass_model,data):
    passed_sample = data
    test_submission_pred = pd.DataFrame()
    for loop_1 in passed_sample:
        pat_id_1 = loop_1
        fname = pat_id_1+".dcm"
        image_path = "stage_2_test_images/"+fname
        ds = dicom.dcmread(image_path)
        img = ds.pixel_array
        img_for_prediction =img[tf.newaxis,...]
        img_pred_128 = np.resize(img,(img_for_prediction.shape[0],128,128,3))
        img_pred_region = pass_model.predict(img_pred_128) # Predict the BBox
        x0 = (img_pred_region[0,0])
        y0 = (img_pred_region[0,1])
        x1 = (img_pred_region[0,2])
        y1 = (img_pred_region[0,3])
        row = pd.Series([pat_id_1,x0*8,y0*8,x1*8,y1*8])
        test_submission_pred = test_submission_pred.append(row, ignore_index = True)
   
    return test_submission_pred


Final_Test_Submission_CSV = test_submission_csv(base_model1,dicom_test_final['PatientID'])

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Final_Test_Submission_CSV.columns = ["Patient_ID","X","Y","Width","Height"]


Final_Test_Submission_CSV.to_csv("Test_Submission_Final.csv",index = False)


Final_Test_Submission_CSV.sample(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()


dicom_test_final.head(1)


def predicted_bound_box(passed_sample):
    # Take a random sample of 5 images and print bounding box patch on images
    sample_data = random.sample(list(Final_Test_Submission_CSV['Patient_ID']), passed_sample)
    if passed_sample/5 == 0:
        z = math.ceil(passed_sample/5)
    else:
        z = math.ceil((passed_sample)/5)

    bounding_box_data = pd.DataFrame()

    fig, ax = plt.subplots(z,5,constrained_layout=True)
    fig.set_figheight(20)
    fig.set_figwidth(20)
    
    i = 0
    j = 0

    for loop_1 in sample_data:
        pat_id_1 = loop_1

        no_ind = dicom_test_final[dicom_test_final['PatientID'] == pat_id_1].index.values
        l_ind = len(no_ind)

        fname = pat_id_1+".dcm"
        image_path = "stage_2_test_images"+"/"+fname
        ds = dicom.dcmread(image_path)
        img = ds.pixel_array

        X = 0
        Y = 0
        width = 0
        height = 0
        rect = []

        for k in range(l_ind):
            X = Final_Test_Submission_CSV.iloc[no_ind[k],1]
            Y = Final_Test_Submission_CSV.iloc[no_ind[k],2]
            width = Final_Test_Submission_CSV.iloc[no_ind[k],3]
            height = Final_Test_Submission_CSV.iloc[no_ind[k],4]
            bounding_box_data = bounding_box_data.append(pd.Series([pat_id_1, l_ind,X,Y,width,height]), ignore_index = True)
            rect.append(patches.Rectangle((X, Y), width, height,linewidth = 3,edgecolor = 'y',facecolor = 'none'))
            ax[i][j].imshow(img, cmap=plt.cm.bone)
            ax[i][j].add_patch(rect[k])

        j = j+1
        if j == 5:
            i = i+1
            j = 0
    plt.figure().clear()
    plt.show()
    bounding_box_data.columns = ['Image Name','No. Of Bounding Boxes','X','Y','Width','Height']
    return(bounding_box_data)


predicted_bound_box(20).style.set_properties(**{'border': '1.3px solid black','color': 'blue'}).hide_index()

<Figure size 640x480 with 0 Axes>

	patientId	x	y	width	height	Target
0	0004cfab-14fd-4e49-80ba-63a80b6bddd6	nan	nan	nan	nan	0
1	00313ee0-9eaa-42f4-b0ab-c148ed3241cd	nan	nan	nan	nan	0
2	00322d4d-1c29-4943-afc9-b6754be640eb	nan	nan	nan	nan	0
3	003d8fa0-6bf1-40ed-b54c-ac657f8495c5	nan	nan	nan	nan	0
4	00436515-870c-4b36-a041-de91049b9ab4	264.000000	152.000000	213.000000	379.000000	1
5	00436515-870c-4b36-a041-de91049b9ab4	562.000000	152.000000	256.000000	453.000000	1
6	00569f44-917d-4c86-a842-81832af98c30	nan	nan	nan	nan	0
7	006cec2e-6ce2-4549-bffa-eadfcd1e9970	nan	nan	nan	nan	0
8	00704310-78a8-4b38-8475-49f4573b2dbb	323.000000	577.000000	160.000000	104.000000	1
9	00704310-78a8-4b38-8475-49f4573b2dbb	695.000000	575.000000	162.000000	137.000000	1
10	008c19e8-a820-403a-930a-bc74a4053664	nan	nan	nan	nan	0
11	009482dc-3db5-48d4-8580-5c89c4f01334	nan	nan	nan	nan	0
12	009eb222-eabc-4150-8121-d5a6d06b8ebf	nan	nan	nan	nan	0
13	00a85be6-6eb0-421d-8acf-ff2dc0007e8a	nan	nan	nan	nan	0
14	00aecb01-a116-45a2-956c-08d2fa55433f	288.000000	322.000000	94.000000	135.000000	1
15	00aecb01-a116-45a2-956c-08d2fa55433f	547.000000	299.000000	119.000000	165.000000	1
16	00c0b293-48e7-4e16-ac76-9269ba535a62	306.000000	544.000000	168.000000	244.000000	1
17	00c0b293-48e7-4e16-ac76-9269ba535a62	650.000000	511.000000	206.000000	284.000000	1
18	00d7c36e-3cdf-4df6-ac03-6c30cdc8e85b	nan	nan	nan	nan	0
19	00f08de1-517e-4652-a04f-d1dc9ee48593	181.000000	184.000000	206.000000	506.000000	1

	patientId	x	y	width	height	Target	class
0	0004cfab-14fd-4e49-80ba-63a80b6bddd6	nan	nan	nan	nan	0	No Lung Opacity / Not Normal
1	00313ee0-9eaa-42f4-b0ab-c148ed3241cd	nan	nan	nan	nan	0	No Lung Opacity / Not Normal
2	00322d4d-1c29-4943-afc9-b6754be640eb	nan	nan	nan	nan	0	No Lung Opacity / Not Normal
3	003d8fa0-6bf1-40ed-b54c-ac657f8495c5	nan	nan	nan	nan	0	Normal
4	00436515-870c-4b36-a041-de91049b9ab4	264.000000	152.000000	213.000000	379.000000	1	Lung Opacity
5	00436515-870c-4b36-a041-de91049b9ab4	264.000000	152.000000	213.000000	379.000000	1	Lung Opacity
6	00436515-870c-4b36-a041-de91049b9ab4	562.000000	152.000000	256.000000	453.000000	1	Lung Opacity
7	00436515-870c-4b36-a041-de91049b9ab4	562.000000	152.000000	256.000000	453.000000	1	Lung Opacity
8	00569f44-917d-4c86-a842-81832af98c30	nan	nan	nan	nan	0	No Lung Opacity / Not Normal
9	006cec2e-6ce2-4549-bffa-eadfcd1e9970	nan	nan	nan	nan	0	No Lung Opacity / Not Normal
10	00704310-78a8-4b38-8475-49f4573b2dbb	323.000000	577.000000	160.000000	104.000000	1	Lung Opacity
11	00704310-78a8-4b38-8475-49f4573b2dbb	323.000000	577.000000	160.000000	104.000000	1	Lung Opacity
12	00704310-78a8-4b38-8475-49f4573b2dbb	695.000000	575.000000	162.000000	137.000000	1	Lung Opacity
13	00704310-78a8-4b38-8475-49f4573b2dbb	695.000000	575.000000	162.000000	137.000000	1	Lung Opacity
14	008c19e8-a820-403a-930a-bc74a4053664	nan	nan	nan	nan	0	No Lung Opacity / Not Normal
15	009482dc-3db5-48d4-8580-5c89c4f01334	nan	nan	nan	nan	0	Normal
16	009eb222-eabc-4150-8121-d5a6d06b8ebf	nan	nan	nan	nan	0	Normal
17	00a85be6-6eb0-421d-8acf-ff2dc0007e8a	nan	nan	nan	nan	0	Normal
18	00aecb01-a116-45a2-956c-08d2fa55433f	288.000000	322.000000	94.000000	135.000000	1	Lung Opacity
19	00aecb01-a116-45a2-956c-08d2fa55433f	288.000000	322.000000	94.000000	135.000000	1	Lung Opacity

	patientId	PredictionString
0	0000a175-0e68-4ca4-b1af-167204a7e0bc	0.5 0 0 100 100
1	0005d3cc-3c3f-40b9-93c3-46231c3eb813	0.5 0 0 100 100
2	000686d7-f4fc-448d-97a0-44fa9c5d3aa6	0.5 0 0 100 100
3	000e3a7d-c0ca-4349-bb26-5af2d8993c3d	0.5 0 0 100 100
4	00100a24-854d-423d-a092-edcf6179e061	0.5 0 0 100 100
5	0015597f-2d69-4bc7-b642-5b5e01534676	0.5 0 0 100 100
6	001b0c51-c7b3-45c1-9c17-fa7594cab96e	0.5 0 0 100 100
7	0022bb50-bf6c-4185-843e-403a9cc1ea80	0.5 0 0 100 100
8	00271e8e-aea8-4f0a-8a34-3025831f1079	0.5 0 0 100 100
9	0028450f-5b8e-4695-9416-8340b6f686b0	0.5 0 0 100 100
10	002bcde0-d8da-4931-ab04-5d724e30261b	0.5 0 0 100 100
11	002fcb77-ef76-4626-ab34-5070f15c20db	0.5 0 0 100 100
12	003206b4-bd4a-4684-8d49-76f4cb713a30	0.5 0 0 100 100
13	00330f7f-d114-4eb2-9c6e-558eeb3084a1	0.5 0 0 100 100
14	00342ae8-ff81-4229-adf6-6a2ab711707b	0.5 0 0 100 100
15	003d17f0-bd8a-485c-bc8b-daec33f53efa	0.5 0 0 100 100
16	003dba79-1b1d-4713-add8-d72c54074f8a	0.5 0 0 100 100
17	003ec9e3-512e-4f6e-923d-daa9f9f3db9a	0.5 0 0 100 100
18	003fbda2-ba55-4714-a03a-83f15bec19e4	0.5 0 0 100 100
19	0041fc67-793c-4129-a952-ea3fb821b445	0.5 0 0 100 100

	x	y	width	height	Target
count	16957.000000	16957.000000	16957.000000	16957.000000	37629.000000
mean	398.980008	360.443121	219.266675	337.799552	0.450636
std	204.869392	149.202409	59.195268	158.986899	0.497564
min	2.000000	2.000000	40.000000	45.000000	0.000000
25%	209.000000	243.000000	178.000000	210.000000	0.000000
50%	343.000000	355.000000	218.000000	309.000000	0.000000
75%	596.000000	472.000000	259.000000	452.000000	1.000000
max	835.000000	881.000000	528.000000	942.000000	1.000000

	patientId	x	y	width	height	Target	class
5	00436515-870c-4b36-a041-de91049b9ab4	264.000000	152.000000	213.000000	379.000000	1	Lung Opacity
7	00436515-870c-4b36-a041-de91049b9ab4	562.000000	152.000000	256.000000	453.000000	1	Lung Opacity
11	00704310-78a8-4b38-8475-49f4573b2dbb	323.000000	577.000000	160.000000	104.000000	1	Lung Opacity
13	00704310-78a8-4b38-8475-49f4573b2dbb	695.000000	575.000000	162.000000	137.000000	1	Lung Opacity
19	00aecb01-a116-45a2-956c-08d2fa55433f	288.000000	322.000000	94.000000	135.000000	1	Lung Opacity
21	00aecb01-a116-45a2-956c-08d2fa55433f	547.000000	299.000000	119.000000	165.000000	1	Lung Opacity
23	00c0b293-48e7-4e16-ac76-9269ba535a62	306.000000	544.000000	168.000000	244.000000	1	Lung Opacity
25	00c0b293-48e7-4e16-ac76-9269ba535a62	650.000000	511.000000	206.000000	284.000000	1	Lung Opacity
28	00f08de1-517e-4652-a04f-d1dc9ee48593	181.000000	184.000000	206.000000	506.000000	1	Lung Opacity
30	00f08de1-517e-4652-a04f-d1dc9ee48593	571.000000	275.000000	230.000000	476.000000	1	Lung Opacity
35	010ccb9f-6d46-4380-af11-84f87397a1b8	652.000000	437.000000	161.000000	293.000000	1	Lung Opacity
37	010ccb9f-6d46-4380-af11-84f87397a1b8	301.000000	405.000000	141.000000	279.000000	1	Lung Opacity
40	012a5620-d082-4bb8-9b3b-e72d8938000c	133.000000	613.000000	275.000000	275.000000	1	Lung Opacity
42	012a5620-d082-4bb8-9b3b-e72d8938000c	678.000000	427.000000	224.000000	340.000000	1	Lung Opacity
47	0174c4bb-28f5-41e3-a13f-a396badc18bd	155.000000	182.000000	273.000000	501.000000	1	Lung Opacity
49	0174c4bb-28f5-41e3-a13f-a396badc18bd	599.000000	220.000000	227.000000	508.000000	1	Lung Opacity
53	019d950b-dd38-4cf3-a686-527a75728be6	229.000000	318.000000	250.000000	301.000000	1	Lung Opacity
55	019d950b-dd38-4cf3-a686-527a75728be6	604.000000	216.000000	196.000000	328.000000	1	Lung Opacity
60	01a6eaa6-222f-4ea8-9874-bbd89dc1a1ce	141.000000	306.000000	225.000000	327.000000	1	Lung Opacity
62	01a6eaa6-222f-4ea8-9874-bbd89dc1a1ce	609.000000	285.000000	236.000000	355.000000	1	Lung Opacity

	Folder	Image_Class	Image_file
0	Training	Lung Opacity	000db696-cf54-4385-b10b-6b16fbb3f985.dcm
1	Training	Lung Opacity	000fe35a-2649-43d4-b027-e67796d412e0.dcm
2	Training	Lung Opacity	001031d9-f904-4a23-b3e5-2c088acd19c6.dcm
3	Training	Lung Opacity	001916b8-3d30-4935-a5d1-8eaddb1646cd.dcm
4	Training	Lung Opacity	0022073f-cec8-42ec-ab5f-bc2314649235.dcm
5	Training	Lung Opacity	002cb550-2e31-42f1-a29d-fbc279977e71.dcm
6	Training	Lung Opacity	00436515-870c-4b36-a041-de91049b9ab4.dcm
7	Training	Lung Opacity	00704310-78a8-4b38-8475-49f4573b2dbb.dcm
8	Training	Lung Opacity	0087bd3a-55a7-4045-b111-b018fa52d361.dcm
9	Training	Lung Opacity	00a05408-8291-4231-886e-13763e103161.dcm
10	Training	Lung Opacity	00aecb01-a116-45a2-956c-08d2fa55433f.dcm
11	Training	Lung Opacity	00c0b293-48e7-4e16-ac76-9269ba535a62.dcm
12	Training	Lung Opacity	00eeb3c9-a892-4fac-a67a-aaa6cc7ffd5c.dcm
13	Training	Lung Opacity	00f08de1-517e-4652-a04f-d1dc9ee48593.dcm
14	Training	Lung Opacity	0100515c-5204-4f31-98e0-f35e4b00004a.dcm
15	Training	Lung Opacity	0101174b-6643-4d4e-b4ba-b6d41d0ce46a.dcm
16	Training	Lung Opacity	010ccb9f-6d46-4380-af11-84f87397a1b8.dcm
17	Training	Lung Opacity	012a5620-d082-4bb8-9b3b-e72d8938000c.dcm
18	Training	Lung Opacity	013c7df0-d66d-4cb1-b3bc-a70085160311.dcm
19	Training	Lung Opacity	0174c4bb-28f5-41e3-a13f-a396badc18bd.dcm

	Folder	Image_Class	Image_file
26664	Training	Not Normal	ffb8998b-f262-4330-820d-bf48a4d8fec1.dcm
26665	Training	Not Normal	ffc01e64-ba14-4620-8016-235fc1609767.dcm
26666	Training	Not Normal	ffcfe8c1-5641-4dc3-910c-573e3227f536.dcm
26667	Training	Not Normal	ffd0c212-bfb6-41e3-b17b-b927b99d1730.dcm
26668	Training	Not Normal	ffd2bc74-f9d6-49fb-84d7-de060ee22583.dcm
26669	Training	Not Normal	ffd670a5-b6dc-4f54-928a-69b7a04662eb.dcm
26670	Training	Not Normal	ffdc771e-1f1a-47f7-b732-3e06e48c24e8.dcm
26671	Training	Not Normal	ffdc957e-6239-427d-8a54-fdf8ced3a356.dcm
26672	Training	Not Normal	ffde3879-241c-4b03-9d91-625cae6b49e8.dcm
26673	Training	Not Normal	ffde3e7e-849c-4077-bfd0-4e4498ee8817.dcm
26674	Training	Not Normal	ffe4707d-517c-4ed0-8a9f-3ad149748991.dcm
26675	Training	Not Normal	ffe9a1c4-634d-408f-9b0f-8098ffb78a4e.dcm
26676	Training	Not Normal	ffe9ab5c-9d39-4235-9ade-d725dcad6b76.dcm
26677	Training	Not Normal	ffeab8ae-f339-40d6-96f1-f1f8c97d2cb1.dcm
26678	Training	Not Normal	ffee0360-20cf-4d4a-9af5-e5c9b493c73b.dcm
26679	Training	Not Normal	fff1cc9c-3895-43be-84e1-a7aaef21002b.dcm
26680	Training	Not Normal	fff7447f-99ce-4102-87f3-9788b2459eb4.dcm
26681	Training	Not Normal	fffc95b5-605b-4226-80ab-62caec682b22.dcm
26682	Training	Not Normal	fffcff11-d018-4414-971a-a7cefa327795.dcm
26683	Training	Not Normal	fffec09e-8a4a-48b1-b33e-ab4890ccd136.dcm

	Folder	Image_Class	Image_file	Full_filename
count	26684	26684	26684	26684
unique	1	3	26684	26684
top	Training	Not Normal	000db696-cf54-4385-b10b-6b16fbb3f985.dcm	Training/Lung Opacity/000db696-cf54-4385-b10b-6b16fbb3f985.dcm
freq	26684	11821	1	1

	Folder	Image_Class	Image_file	Full_filename	Image_Class_Category	Image_Target
15613	Training	Not Normal	1f929e8d-0289-4712-806b-8894c2ba2f93.dcm	Training/Not Normal/1f929e8d-0289-4712-806b-8894c2ba2f93.dcm	2	0
21995	Training	Not Normal	a616c756-c1f4-4c3b-af81-cc51695d4fe4.dcm	Training/Not Normal/a616c756-c1f4-4c3b-af81-cc51695d4fe4.dcm	2	0
21703	Training	Not Normal	a03bf35b-b57b-4cd0-bac4-c5d7c13a168a.dcm	Training/Not Normal/a03bf35b-b57b-4cd0-bac4-c5d7c13a168a.dcm	2	0
6639	Training	Normal	197146c0-dfe6-4938-ab89-5fbef34150fc.dcm	Training/Normal/197146c0-dfe6-4938-ab89-5fbef34150fc.dcm	1	0
2660	Training	Lung Opacity	7860706e-b07d-4c69-b17c-03ca4f8e8a95.dcm	Training/Lung Opacity/7860706e-b07d-4c69-b17c-03ca4f8e8a95.dcm	0	1
18912	Training	Not Normal	6a17292a-fcef-423b-a7a2-046b6923d559.dcm	Training/Not Normal/6a17292a-fcef-423b-a7a2-046b6923d559.dcm	2	0
5445	Training	Lung Opacity	de304f9b-f407-43d5-89b9-bdb07f8308b1.dcm	Training/Lung Opacity/de304f9b-f407-43d5-89b9-bdb07f8308b1.dcm	0	1
23503	Training	Not Normal	cca50e52-2b44-40ba-84e7-430aa3ff3f98.dcm	Training/Not Normal/cca50e52-2b44-40ba-84e7-430aa3ff3f98.dcm	2	0
627	Training	Lung Opacity	1a2cef8e-99cb-43cc-b9b5-65fe5104037f.dcm	Training/Lung Opacity/1a2cef8e-99cb-43cc-b9b5-65fe5104037f.dcm	0	1
20246	Training	Not Normal	818a1ce8-68ef-4cdd-b92d-f09ba72c692b.dcm	Training/Not Normal/818a1ce8-68ef-4cdd-b92d-f09ba72c692b.dcm	2	0
2295	Training	Lung Opacity	67949b55-7b2b-41be-becc-70f86797db4e.dcm	Training/Lung Opacity/67949b55-7b2b-41be-becc-70f86797db4e.dcm	0	1
13155	Training	Normal	d341810f-864a-4f08-8da2-c8b0896d6db7.dcm	Training/Normal/d341810f-864a-4f08-8da2-c8b0896d6db7.dcm	1	0
10606	Training	Normal	8fae133a-b424-48b1-96a8-a2add7b8b8a2.dcm	Training/Normal/8fae133a-b424-48b1-96a8-a2add7b8b8a2.dcm	1	0
9259	Training	Normal	6dce4fb8-2166-4f50-be8a-8610eab74026.dcm	Training/Normal/6dce4fb8-2166-4f50-be8a-8610eab74026.dcm	1	0
16778	Training	Not Normal	467ba515-37f4-4014-9fb1-a981daa58286.dcm	Training/Not Normal/467ba515-37f4-4014-9fb1-a981daa58286.dcm	2	0
12063	Training	Normal	b3c3a0b7-e29b-4e0c-ba93-8c00c26ba5ad.dcm	Training/Normal/b3c3a0b7-e29b-4e0c-ba93-8c00c26ba5ad.dcm	1	0
16611	Training	Not Normal	440e0b82-41f4-4b08-a0e3-405a2831a7e0.dcm	Training/Not Normal/440e0b82-41f4-4b08-a0e3-405a2831a7e0.dcm	2	0
6237	Training	Normal	09c0e0e5-e269-4d0c-89ea-59d1a77e2097.dcm	Training/Normal/09c0e0e5-e269-4d0c-89ea-59d1a77e2097.dcm	1	0
22687	Training	Not Normal	bb6291b5-9f76-4f55-80d2-45edf39b85be.dcm	Training/Not Normal/bb6291b5-9f76-4f55-80d2-45edf39b85be.dcm	2	0
13765	Training	Normal	e20c5b46-c709-49e9-972a-86d845c1defc.dcm	Training/Normal/e20c5b46-c709-49e9-972a-86d845c1defc.dcm	1	0

	SpecificCharacterSet	SOPClassUID	SOPInstanceUID	StudyDate	AccessionNumber	Modality	ConversionType	ReferringPhysicianName	SeriesDescription	PatientName	PatientID	PatientBirthDate	PatientSex	PatientAge	BodyPartExamined	ViewPosition	StudyInstanceUID	SeriesInstanceUID	StudyID	SeriesNumber	InstanceNumber	PatientOrientation	SamplesPerPixel	PhotometricInterpretation	Rows	Columns	MultiPixelSpacing	PixelSpacing	PixelSpacing1	BitsAllocated	BitsStored	HighBit	LossyImageCompression	LossyImageCompressionMethod	fname	img_min	img_max	img_mean	img_std	img_pct_window
10461	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.11678.1517874359.879033	19010101	nan	CR	WSD	nan	view: AP	7200a1e9-eba0-4ced-bdd3-88544f162af0	7200a1e9-eba0-4ced-bdd3-88544f162af0	nan	M	22	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.11678.1517874359.879032	1.2.276.0.7230010.3.1.3.8323329.11678.1517874359.879031	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.139000	0.139000	8	8	7	1	ISO_10918_1	stage_2_train_images\7200a1e9-eba0-4ced-bdd3-88544f162af0.dcm	0	-1	104.294415	60.594108	0.318659
16693	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.30440.1517874499.861241	19010101	nan	CR	WSD	nan	view: AP	a8e8e360-1c06-4932-820f-cc0066da187e	a8e8e360-1c06-4932-820f-cc0066da187e	nan	F	66	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.30440.1517874499.861240	1.2.276.0.7230010.3.1.3.8323329.30440.1517874499.861239	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.139000	0.139000	8	8	7	1	ISO_10918_1	stage_2_train_images\a8e8e360-1c06-4932-820f-cc0066da187e.dcm	0	-1	127.899700	53.511569	0.164686
9149	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.4081.1517874305.819620	19010101	nan	CR	WSD	nan	view: PA	672a7b2f-f672-46c5-b876-bad17cd8a567	672a7b2f-f672-46c5-b876-bad17cd8a567	nan	F	34	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.4081.1517874305.819619	1.2.276.0.7230010.3.1.3.8323329.4081.1517874305.819618	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.194311	0.194311	8	8	7	1	ISO_10918_1	stage_2_train_images\672a7b2f-f672-46c5-b876-bad17cd8a567.dcm	0	-105	111.929241	28.378863	0.162151
18352	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.29517.1517874492.355669	19010101	nan	CR	WSD	nan	view: PA	b7637d3c-fa05-45c1-816a-1bbb95f07af1	b7637d3c-fa05-45c1-816a-1bbb95f07af1	nan	F	48	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.29517.1517874492.355668	1.2.276.0.7230010.3.1.3.8323329.29517.1517874492.355667	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.194311	0.194311	8	8	7	1	ISO_10918_1	stage_2_train_images\b7637d3c-fa05-45c1-816a-1bbb95f07af1.dcm	0	-1	101.650616	42.187196	0.139111
7953	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.9763.1517874344.754173	19010101	nan	CR	WSD	nan	view: PA	5d2bf120-562a-484a-a2db-65953e2fc734	5d2bf120-562a-484a-a2db-65953e2fc734	nan	M	70	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.9763.1517874344.754172	1.2.276.0.7230010.3.1.3.8323329.9763.1517874344.754171	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.168000	0.168000	8	8	7	1	ISO_10918_1	stage_2_train_images\5d2bf120-562a-484a-a2db-65953e2fc734.dcm	5	-1	180.103366	49.422149	0.044318
25042	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.21164.1517874432.567653	19010101	nan	CR	WSD	nan	view: PA	f1d6dbca-db29-41d4-9b6f-2b30fd6e9f69	f1d6dbca-db29-41d4-9b6f-2b30fd6e9f69	nan	M	20	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.21164.1517874432.567652	1.2.276.0.7230010.3.1.3.8323329.21164.1517874432.567651	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.143000	0.143000	8	8	7	1	ISO_10918_1	stage_2_train_images\f1d6dbca-db29-41d4-9b6f-2b30fd6e9f69.dcm	0	-1	106.378491	71.737520	0.247437
9792	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.21830.1517874437.331503	19010101	nan	CR	WSD	nan	view: PA	6c5b9330-09d6-4181-ba38-afcbbc00dc3b	6c5b9330-09d6-4181-ba38-afcbbc00dc3b	nan	F	56	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.21830.1517874437.331502	1.2.276.0.7230010.3.1.3.8323329.21830.1517874437.331501	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.143000	0.143000	8	8	7	1	ISO_10918_1	stage_2_train_images\6c5b9330-09d6-4181-ba38-afcbbc00dc3b.dcm	0	-11	124.677175	75.587729	0.278798
2847	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.26704.1517874473.667762	19010101	nan	CR	WSD	nan	view: PA	304cd9fa-df87-4fb1-bf38-53a130dfa025	304cd9fa-df87-4fb1-bf38-53a130dfa025	nan	M	56	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.26704.1517874473.667761	1.2.276.0.7230010.3.1.3.8323329.26704.1517874473.667760	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.143000	0.143000	8	8	7	1	ISO_10918_1	stage_2_train_images\304cd9fa-df87-4fb1-bf38-53a130dfa025.dcm	0	-9	124.546081	68.109369	0.294114
20320	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.18186.1517874409.138029	19010101	nan	CR	WSD	nan	view: AP	c8f8c1af-28f3-420d-85d6-9501a8002097	c8f8c1af-28f3-420d-85d6-9501a8002097	nan	M	20	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.18186.1517874409.138028	1.2.276.0.7230010.3.1.3.8323329.18186.1517874409.138027	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.139000	0.139000	8	8	7	1	ISO_10918_1	stage_2_train_images\c8f8c1af-28f3-420d-85d6-9501a8002097.dcm	0	-1	96.371264	53.526445	0.274091
2186	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.13551.1517874373.268603	19010101	nan	CR	WSD	nan	view: AP	1fcc4272-c40e-447c-a15f-6cdf977716a1	1fcc4272-c40e-447c-a15f-6cdf977716a1	nan	F	43	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.13551.1517874373.268602	1.2.276.0.7230010.3.1.3.8323329.13551.1517874373.268601	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.171000	0.171000	8	8	7	1	ISO_10918_1	stage_2_train_images\1fcc4272-c40e-447c-a15f-6cdf977716a1.dcm	0	-20	133.794801	51.329732	0.161374
15228	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.11844.1517874361.458350	19010101	nan	CR	WSD	nan	view: AP	9c6b1f79-c2e8-497a-88aa-34af955ce666	9c6b1f79-c2e8-497a-88aa-34af955ce666	nan	M	60	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.11844.1517874361.458349	1.2.276.0.7230010.3.1.3.8323329.11844.1517874361.458348	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.171000	0.171000	8	8	7	1	ISO_10918_1	stage_2_train_images\9c6b1f79-c2e8-497a-88aa-34af955ce666.dcm	0	-42	135.740139	38.510586	0.052581
6164	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.5780.1517874318.157929	19010101	nan	CR	WSD	nan	view: AP	4d7c5fb9-7776-4ab5-a983-13bba41b39e6	4d7c5fb9-7776-4ab5-a983-13bba41b39e6	nan	F	73	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.5780.1517874318.157928	1.2.276.0.7230010.3.1.3.8323329.5780.1517874318.157927	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.139000	0.139000	8	8	7	1	ISO_10918_1	stage_2_train_images\4d7c5fb9-7776-4ab5-a983-13bba41b39e6.dcm	0	-1	130.129486	52.586636	0.150147
23123	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.9904.1517874345.620268	19010101	nan	CR	WSD	nan	view: PA	e1bcfa18-5fed-4a92-85fb-14438a17719b	e1bcfa18-5fed-4a92-85fb-14438a17719b	nan	M	56	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.9904.1517874345.620267	1.2.276.0.7230010.3.1.3.8323329.9904.1517874345.620266	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.143000	0.143000	8	8	7	1	ISO_10918_1	stage_2_train_images\e1bcfa18-5fed-4a92-85fb-14438a17719b.dcm	0	-1	127.952350	64.711431	0.250209
15075	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.8515.1517874337.843655	19010101	nan	CR	WSD	nan	view: AP	9aff4c83-b9ce-407b-aee1-9456acb46d97	9aff4c83-b9ce-407b-aee1-9456acb46d97	nan	M	68	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.8515.1517874337.843654	1.2.276.0.7230010.3.1.3.8323329.8515.1517874337.843653	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.171000	0.171000	8	8	7	1	ISO_10918_1	stage_2_train_images\9aff4c83-b9ce-407b-aee1-9456acb46d97.dcm	0	-18	145.779552	41.191552	0.061395
25586	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.9078.1517874340.993622	19010101	nan	CR	WSD	nan	view: PA	f6ae6a07-2f67-49cc-92ac-c84daae85ba3	f6ae6a07-2f67-49cc-92ac-c84daae85ba3	nan	M	68	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.9078.1517874340.993621	1.2.276.0.7230010.3.1.3.8323329.9078.1517874340.993620	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.143000	0.143000	8	8	7	1	ISO_10918_1	stage_2_train_images\f6ae6a07-2f67-49cc-92ac-c84daae85ba3.dcm	0	-3	122.697148	65.806944	0.278937
21550	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.17979.1517874407.857075	19010101	nan	CR	WSD	nan	view: PA	d3e9cd40-d281-46dd-a115-87b9c806ca19	d3e9cd40-d281-46dd-a115-87b9c806ca19	nan	F	55	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.17979.1517874407.857074	1.2.276.0.7230010.3.1.3.8323329.17979.1517874407.857073	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.143000	0.143000	8	8	7	1	ISO_10918_1	stage_2_train_images\d3e9cd40-d281-46dd-a115-87b9c806ca19.dcm	0	-5	129.292119	62.033524	0.174953
13211	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.10384.1517874351.684129	19010101	nan	CR	WSD	nan	view: PA	8abcbbbd-3aa8-4dac-b1cd-932a7d0a072c	8abcbbbd-3aa8-4dac-b1cd-932a7d0a072c	nan	M	32	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.10384.1517874351.684128	1.2.276.0.7230010.3.1.3.8323329.10384.1517874351.684127	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.143000	0.143000	8	8	7	1	ISO_10918_1	stage_2_train_images\8abcbbbd-3aa8-4dac-b1cd-932a7d0a072c.dcm	0	-3	118.586931	62.055935	0.276836
6548	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.30441.1517874499.884752	19010101	nan	CR	WSD	nan	view: PA	50a23843-cbdb-4570-9033-fdd9e015d9e9	50a23843-cbdb-4570-9033-fdd9e015d9e9	nan	M	54	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.30441.1517874499.884751	1.2.276.0.7230010.3.1.3.8323329.30441.1517874499.884750	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.143000	0.143000	8	8	7	1	ISO_10918_1	stage_2_train_images\50a23843-cbdb-4570-9033-fdd9e015d9e9.dcm	0	-1	112.561371	68.214553	0.349430
6482	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.28543.1517874485.845932	19010101	nan	CR	WSD	nan	view: AP	5013133b-e0dc-4c38-9350-8fd512c09a97	5013133b-e0dc-4c38-9350-8fd512c09a97	nan	M	60	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.28543.1517874485.845931	1.2.276.0.7230010.3.1.3.8323329.28543.1517874485.845930	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.171000	0.171000	8	8	7	1	ISO_10918_1	stage_2_train_images\5013133b-e0dc-4c38-9350-8fd512c09a97.dcm	0	-27	132.001262	60.921683	0.180203
4338	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.4823.1517874309.957480	19010101	nan	CR	WSD	nan	view: PA	3d6654d3-b967-4de5-8e27-7a1c18588dee	3d6654d3-b967-4de5-8e27-7a1c18588dee	nan	M	54	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.4823.1517874309.957479	1.2.276.0.7230010.3.1.3.8323329.4823.1517874309.957478	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.143000	0.143000	8	8	7	1	ISO_10918_1	stage_2_train_images\3d6654d3-b967-4de5-8e27-7a1c18588dee.dcm	0	-6	112.102633	69.223339	0.300241

	patientId	x	y	width	height	Target	class	SpecificCharacterSet	SOPClassUID	SOPInstanceUID	StudyDate	AccessionNumber	Modality	ConversionType	ReferringPhysicianName	SeriesDescription	PatientName	PatientBirthDate	PatientSex	PatientAge	BodyPartExamined	ViewPosition	StudyInstanceUID	SeriesInstanceUID	StudyID	SeriesNumber	InstanceNumber	PatientOrientation	SamplesPerPixel	PhotometricInterpretation	Rows	Columns	MultiPixelSpacing	PixelSpacing	PixelSpacing1	BitsAllocated	BitsStored	HighBit	LossyImageCompression	LossyImageCompressionMethod	fname	img_min	img_max	img_mean	img_std	img_pct_window
9548	6850e324-6105-4e8e-961e-6ae2f5e71e66	nan	nan	nan	nan	0	Normal	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.11171.1517874356.976841	19010101	nan	CR	WSD	nan	view: PA	6850e324-6105-4e8e-961e-6ae2f5e71e66	nan	F	48	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.11171.1517874356.976840	1.2.276.0.7230010.3.1.3.8323329.11171.1517874356.976839	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.143000	0.143000	8	8	7	1	ISO_10918_1	stage_2_train_images\6850e324-6105-4e8e-961e-6ae2f5e71e66.dcm	0	-4	92.979630	72.874497	0.369698
1636	17aba977-7b64-47b0-acb0-d41abba1fcbc	nan	nan	nan	nan	0	Normal	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.10787.1517874354.42396	19010101	nan	CR	WSD	nan	view: PA	17aba977-7b64-47b0-acb0-d41abba1fcbc	nan	F	27	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.10787.1517874354.42395	1.2.276.0.7230010.3.1.3.8323329.10787.1517874354.42394	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.139000	0.139000	8	8	7	1	ISO_10918_1	stage_2_train_images\17aba977-7b64-47b0-acb0-d41abba1fcbc.dcm	0	-32	130.799788	54.465151	0.130149
17309	a5b3340e-0054-48c2-9966-fe9e2af21da7	nan	nan	nan	nan	0	No Lung Opacity / Not Normal	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.24477.1517874454.716740	19010101	nan	CR	WSD	nan	view: PA	a5b3340e-0054-48c2-9966-fe9e2af21da7	nan	F	43	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.24477.1517874454.716739	1.2.276.0.7230010.3.1.3.8323329.24477.1517874454.716738	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.143000	0.143000	8	8	7	1	ISO_10918_1	stage_2_train_images\a5b3340e-0054-48c2-9966-fe9e2af21da7.dcm	0	-7	150.781827	69.885828	0.174041
10714	71576c22-51ce-4510-98d8-6933e0b9b810	279.000000	301.000000	160.000000	297.000000	1	Lung Opacity	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.14071.1517874377.339450	19010101	nan	CR	WSD	nan	view: AP	71576c22-51ce-4510-98d8-6933e0b9b810	nan	M	25	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.14071.1517874377.339449	1.2.276.0.7230010.3.1.3.8323329.14071.1517874377.339448	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.168000	0.168000	8	8	7	1	ISO_10918_1	stage_2_train_images\71576c22-51ce-4510-98d8-6933e0b9b810.dcm	0	-1	95.796071	61.864365	0.230841
18086	ab9069f0-1511-414d-b37a-2920517004d4	48.000000	692.000000	240.000000	281.000000	1	Lung Opacity	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.1571.1517874291.616328	19010101	nan	CR	WSD	nan	view: AP	ab9069f0-1511-414d-b37a-2920517004d4	nan	F	52	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.1571.1517874291.616327	1.2.276.0.7230010.3.1.3.8323329.1571.1517874291.616326	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.168000	0.168000	8	8	7	1	ISO_10918_1	stage_2_train_images\ab9069f0-1511-414d-b37a-2920517004d4.dcm	0	-23	125.423249	49.520973	0.151056
20890	be03b6e9-e030-4491-b23e-ac1d7efc73bb	254.000000	150.000000	290.000000	272.000000	1	Lung Opacity	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.7469.1517874331.758564	19010101	nan	CR	WSD	nan	view: AP	be03b6e9-e030-4491-b23e-ac1d7efc73bb	nan	M	63	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.7469.1517874331.758563	1.2.276.0.7230010.3.1.3.8323329.7469.1517874331.758562	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.139000	0.139000	8	8	7	1	ISO_10918_1	stage_2_train_images\be03b6e9-e030-4491-b23e-ac1d7efc73bb.dcm	0	-1	132.337021	49.335950	0.149296
22523	cb9149e2-a09d-4b4a-a644-81d8f394d679	nan	nan	nan	nan	0	Normal	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.31000.1517874505.636643	19010101	nan	CR	WSD	nan	view: AP	cb9149e2-a09d-4b4a-a644-81d8f394d679	nan	M	43	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.31000.1517874505.636642	1.2.276.0.7230010.3.1.3.8323329.31000.1517874505.636641	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.139000	0.139000	8	8	7	1	ISO_10918_1	stage_2_train_images\cb9149e2-a09d-4b4a-a644-81d8f394d679.dcm	0	-1	104.242820	43.270342	0.247639
9396	66fbe46c-488b-4571-9c74-ee8742f09ccb	671.000000	250.000000	148.000000	251.000000	1	Lung Opacity	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.22260.1517874439.791321	19010101	nan	CR	WSD	nan	view: AP	66fbe46c-488b-4571-9c74-ee8742f09ccb	nan	F	52	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.22260.1517874439.791320	1.2.276.0.7230010.3.1.3.8323329.22260.1517874439.791319	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.168000	0.168000	8	8	7	1	ISO_10918_1	stage_2_train_images\66fbe46c-488b-4571-9c74-ee8742f09ccb.dcm	0	-1	110.947284	50.607818	0.242702
2186	2a4489f6-6f7b-46f5-a937-281206307943	226.000000	367.000000	168.000000	343.000000	1	Lung Opacity	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.5947.1517874319.184400	19010101	nan	CR	WSD	nan	view: PA	2a4489f6-6f7b-46f5-a937-281206307943	nan	M	38	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.5947.1517874319.184399	1.2.276.0.7230010.3.1.3.8323329.5947.1517874319.184398	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.168000	0.168000	8	8	7	1	ISO_10918_1	stage_2_train_images\2a4489f6-6f7b-46f5-a937-281206307943.dcm	12	-5	171.498249	53.161424	0.048636
29162	0582f503-a451-448f-87a5-a67589c5f7c5	nan	nan	nan	nan	0	Normal	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.16732.1517874399.233064	19010101	nan	CR	WSD	nan	view: PA	0582f503-a451-448f-87a5-a67589c5f7c5	nan	F	36	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.16732.1517874399.233063	1.2.276.0.7230010.3.1.3.8323329.16732.1517874399.233062	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.194308	0.194308	8	8	7	1	ISO_10918_1	stage_2_train_images\0582f503-a451-448f-87a5-a67589c5f7c5.dcm	0	-17	110.400094	28.864804	0.171775
27564	f4c33ee7-952c-44df-b966-69ebc9118efa	nan	nan	nan	nan	0	No Lung Opacity / Not Normal	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.26073.1517874469.278440	19010101	nan	CR	WSD	nan	view: AP	f4c33ee7-952c-44df-b966-69ebc9118efa	nan	M	25	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.26073.1517874469.278439	1.2.276.0.7230010.3.1.3.8323329.26073.1517874469.278438	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.171000	0.171000	8	8	7	1	ISO_10918_1	stage_2_train_images\f4c33ee7-952c-44df-b966-69ebc9118efa.dcm	0	-12	142.632413	40.547089	0.051129
1737	1854a99c-bfc7-4f4e-8396-c97eebb20106	nan	nan	nan	nan	0	Normal	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.12646.1517874366.831723	19010101	nan	CR	WSD	nan	view: PA	1854a99c-bfc7-4f4e-8396-c97eebb20106	nan	M	73	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.12646.1517874366.831722	1.2.276.0.7230010.3.1.3.8323329.12646.1517874366.831721	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.143000	0.143000	8	8	7	1	ISO_10918_1	stage_2_train_images\1854a99c-bfc7-4f4e-8396-c97eebb20106.dcm	0	-10	119.363234	73.255130	0.238959
25932	e7b3fd9c-d51f-42dd-9f90-25160990faa8	564.000000	274.000000	223.000000	419.000000	1	Lung Opacity	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.17068.1517874401.160928	19010101	nan	CR	WSD	nan	view: AP	e7b3fd9c-d51f-42dd-9f90-25160990faa8	nan	M	45	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.17068.1517874401.160927	1.2.276.0.7230010.3.1.3.8323329.17068.1517874401.160926	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.139000	0.139000	8	8	7	1	ISO_10918_1	stage_2_train_images\e7b3fd9c-d51f-42dd-9f90-25160990faa8.dcm	0	-1	113.844486	64.594378	0.258999
10468	6f491520-b891-48e4-9047-7afc2ad9f7bb	nan	nan	nan	nan	0	No Lung Opacity / Not Normal	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.30636.1517874503.546090	19010101	nan	CR	WSD	nan	view: PA	6f491520-b891-48e4-9047-7afc2ad9f7bb	nan	F	45	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.30636.1517874503.546089	1.2.276.0.7230010.3.1.3.8323329.30636.1517874503.546088	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.143000	0.143000	8	8	7	1	ISO_10918_1	stage_2_train_images\6f491520-b891-48e4-9047-7afc2ad9f7bb.dcm	0	-1	107.142705	71.923364	0.373092
13884	8aa0828d-eff2-4791-a38e-37c3f533f4c0	631.000000	255.000000	264.000000	411.000000	1	Lung Opacity	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.11273.1517874357.558631	19010101	nan	CR	WSD	nan	view: AP	8aa0828d-eff2-4791-a38e-37c3f533f4c0	nan	M	44	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.11273.1517874357.558630	1.2.276.0.7230010.3.1.3.8323329.11273.1517874357.558629	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.168000	0.168000	8	8	7	1	ISO_10918_1	stage_2_train_images\8aa0828d-eff2-4791-a38e-37c3f533f4c0.dcm	0	-19	160.229908	53.317672	0.101672
4671	407bc983-cb9f-4559-9e82-8b559e466042	554.000000	423.000000	224.000000	338.000000	1	Lung Opacity	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.8584.1517874338.248191	19010101	nan	CR	WSD	nan	view: AP	407bc983-cb9f-4559-9e82-8b559e466042	nan	F	25	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.8584.1517874338.248190	1.2.276.0.7230010.3.1.3.8323329.8584.1517874338.248189	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.168000	0.168000	8	8	7	1	ISO_10918_1	stage_2_train_images\407bc983-cb9f-4559-9e82-8b559e466042.dcm	0	-44	113.122311	46.937807	0.209690
7680	59010e44-e84f-45c4-890b-dfaa5baca9cc	nan	nan	nan	nan	0	No Lung Opacity / Not Normal	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.16397.1517874397.345873	19010101	nan	CR	WSD	nan	view: AP	59010e44-e84f-45c4-890b-dfaa5baca9cc	nan	M	77	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.16397.1517874397.345872	1.2.276.0.7230010.3.1.3.8323329.16397.1517874397.345871	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.139000	0.139000	8	8	7	1	ISO_10918_1	stage_2_train_images\59010e44-e84f-45c4-890b-dfaa5baca9cc.dcm	0	-1	123.783945	57.970570	0.211861
24298	da490852-2373-427d-8384-9c493ce53dc6	nan	nan	nan	nan	0	No Lung Opacity / Not Normal	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.11241.1517874357.368618	19010101	nan	CR	WSD	nan	view: PA	da490852-2373-427d-8384-9c493ce53dc6	nan	F	34	CHEST	PA	1.2.276.0.7230010.3.1.2.8323329.11241.1517874357.368617	1.2.276.0.7230010.3.1.3.8323329.11241.1517874357.368616	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.168000	0.168000	8	8	7	1	ISO_10918_1	stage_2_train_images\da490852-2373-427d-8384-9c493ce53dc6.dcm	0	-1	156.739548	69.235548	0.074763
13854	8a67bf0f-a6f3-414b-8c0b-16230219055d	nan	nan	nan	nan	0	No Lung Opacity / Not Normal	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.14437.1517874379.407805	19010101	nan	CR	WSD	nan	view: AP	8a67bf0f-a6f3-414b-8c0b-16230219055d	nan	M	47	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.14437.1517874379.407804	1.2.276.0.7230010.3.1.3.8323329.14437.1517874379.407803	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.139000	0.139000	8	8	7	1	ISO_10918_1	stage_2_train_images\8a67bf0f-a6f3-414b-8c0b-16230219055d.dcm	0	-1	117.263693	59.230636	0.277646
5654	4885207c-0763-45f4-b2ac-ea5658e3202f	nan	nan	nan	nan	0	Normal	ISO_IR 100	1.2.840.10008.5.1.4.1.1.7	1.2.276.0.7230010.3.1.4.8323329.4829.1517874309.981341	19010101	nan	CR	WSD	nan	view: AP	4885207c-0763-45f4-b2ac-ea5658e3202f	nan	M	60	CHEST	AP	1.2.276.0.7230010.3.1.2.8323329.4829.1517874309.981340	1.2.276.0.7230010.3.1.3.8323329.4829.1517874309.981339	nan	1	1	nan	1	MONOCHROME2	1024	1024	1	0.171000	0.171000	8	8	7	1	ISO_10918_1	stage_2_train_images\4885207c-0763-45f4-b2ac-ea5658e3202f.dcm	0	-21	138.056235	49.979666	0.113164

	Folder	Image_Class	Image_file	Full_filename	Image_Class_Category	Image_Target
14756	Training	Normal	fd7047e2-1865-4f07-89fa-75a86c79409a.dcm	Training/Normal/fd7047e2-1865-4f07-89fa-75a86c79409a.dcm	1	0
1492	Training	Lung Opacity	3c4a89ed-4338-4410-8848-a0bf226f6e21.dcm	Training/Lung Opacity/3c4a89ed-4338-4410-8848-a0bf226f6e21.dcm	0	1
22238	Training	Not Normal	aa93c84b-e688-4e5e-a950-78dc063bcabc.dcm	Training/Not Normal/aa93c84b-e688-4e5e-a950-78dc063bcabc.dcm	2	0
5304	Training	Lung Opacity	d5be0945-b546-4640-b0a0-d2dd209122e8.dcm	Training/Lung Opacity/d5be0945-b546-4640-b0a0-d2dd209122e8.dcm	0	1
1441	Training	Lung Opacity	3b3d0da3-cfb5-42c8-a195-6ac03626a11f.dcm	Training/Lung Opacity/3b3d0da3-cfb5-42c8-a195-6ac03626a11f.dcm	0	1
18620	Training	Not Normal	654b2bfc-5d84-42f8-8f8c-61288c661242.dcm	Training/Not Normal/654b2bfc-5d84-42f8-8f8c-61288c661242.dcm	2	0
4811	Training	Lung Opacity	be2a8801-3cc0-4c24-a73a-a1e13ff94948.dcm	Training/Lung Opacity/be2a8801-3cc0-4c24-a73a-a1e13ff94948.dcm	0	1
5480	Training	Lung Opacity	e0af96b5-9e91-4cc7-84c7-a3d04d4c9405.dcm	Training/Lung Opacity/e0af96b5-9e91-4cc7-84c7-a3d04d4c9405.dcm	0	1
23480	Training	Not Normal	cc548ac0-8784-4430-86af-02e1c6bc36c1.dcm	Training/Not Normal/cc548ac0-8784-4430-86af-02e1c6bc36c1.dcm	2	0
3243	Training	Lung Opacity	9341907e-9b62-4197-a0b3-3ddd3c55a884.dcm	Training/Lung Opacity/9341907e-9b62-4197-a0b3-3ddd3c55a884.dcm	0	1
7622	Training	Normal	44e4beae-e4d8-432a-9d99-e21efc4664db.dcm	Training/Normal/44e4beae-e4d8-432a-9d99-e21efc4664db.dcm	1	0
1237	Training	Lung Opacity	36af11b1-75ca-473d-b29c-4bfa1802a81e.dcm	Training/Lung Opacity/36af11b1-75ca-473d-b29c-4bfa1802a81e.dcm	0	1
5689	Training	Lung Opacity	ed6e3fd5-ac11-4733-befd-aac7c6db0a7b.dcm	Training/Lung Opacity/ed6e3fd5-ac11-4733-befd-aac7c6db0a7b.dcm	0	1
23755	Training	Not Normal	d086792f-5d97-473c-b0f3-291385da233f.dcm	Training/Not Normal/d086792f-5d97-473c-b0f3-291385da233f.dcm	2	0
21890	Training	Not Normal	a441e005-54e9-469a-b3ce-5c94a5e42c8c.dcm	Training/Not Normal/a441e005-54e9-469a-b3ce-5c94a5e42c8c.dcm	2	0
10954	Training	Normal	97a54b60-1f60-4d36-a43a-ef4c19904ab1.dcm	Training/Normal/97a54b60-1f60-4d36-a43a-ef4c19904ab1.dcm	1	0
6469	Training	Normal	153c4961-5c28-448a-9dc0-c972ce580671.dcm	Training/Normal/153c4961-5c28-448a-9dc0-c972ce580671.dcm	1	0
16073	Training	Not Normal	38062f40-82c9-4322-b2bb-dacca835bb3b.dcm	Training/Not Normal/38062f40-82c9-4322-b2bb-dacca835bb3b.dcm	2	0
24990	Training	Not Normal	e59245f7-7879-4452-bf2c-d986d453b5ef.dcm	Training/Not Normal/e59245f7-7879-4452-bf2c-d986d453b5ef.dcm	2	0
25709	Training	Not Normal	f0fb164f-f564-434b-a3df-f74e37394ef7.dcm	Training/Not Normal/f0fb164f-f564-434b-a3df-f74e37394ef7.dcm	2	0

	count	unique	top	freq
patientId	30227	26684	3239951b-6211-4290-b237-3d9ad17176db	4
class	30227	3	No Lung Opacity / Not Normal	11821
SpecificCharacterSet	30227	1	ISO_IR 100	30227
SOPClassUID	30227	1	1.2.840.10008.5.1.4.1.1.7	30227
SOPInstanceUID	30227	26684	1.2.276.0.7230010.3.1.4.8323329.2740.1517874298.197769	4
Modality	30227	1	CR	30227
ConversionType	30227	1	WSD	30227
SeriesDescription	30227	2	view: AP	15297
PatientName	30227	26684	3239951b-6211-4290-b237-3d9ad17176db	4
PatientSex	30227	2	M	17216
BodyPartExamined	30227	1	CHEST	30227
ViewPosition	30227	2	AP	15297
StudyInstanceUID	30227	26684	1.2.276.0.7230010.3.1.2.8323329.2740.1517874298.197768	4
SeriesInstanceUID	30227	26684	1.2.276.0.7230010.3.1.3.8323329.2740.1517874298.197767	4
PhotometricInterpretation	30227	1	MONOCHROME2	30227
LossyImageCompressionMethod	30227	1	ISO_10918_1	30227
fname	30227	26684	stage_2_train_images\3239951b-6211-4290-b237-3d9ad17176db.dcm	4
AGE_GROUP	30227	6	41-60	13117

	count	mean	std	min	25%	50%	75%	max
x	9555.000000	394.047724	204.574172	2.000000	207.000000	324.000000	594.000000	835.000000
y	9555.000000	366.839560	148.940488	2.000000	249.000000	365.000000	478.500000	881.000000
width	9555.000000	218.471376	59.289475	40.000000	177.000000	217.000000	259.000000	528.000000
height	9555.000000	329.269702	157.750755	45.000000	203.000000	298.000000	438.000000	942.000000
Target	30227.000000	0.316108	0.464963	0.000000	0.000000	0.000000	1.000000	1.000000
StudyDate	30227.000000	19010101.000000	0.000000	19010101.000000	19010101.000000	19010101.000000	19010101.000000	19010101.000000
StudyTime	30227.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
AccessionNumber	0.000000	nan	nan	nan	nan	nan	nan	nan
ReferringPhysicianName	0.000000	nan	nan	nan	nan	nan	nan	nan
PatientBirthDate	0.000000	nan	nan	nan	nan	nan	nan	nan
PatientAge	30227.000000	46.797764	16.892940	1.000000	34.000000	49.000000	59.000000	155.000000
StudyID	0.000000	nan	nan	nan	nan	nan	nan	nan
SeriesNumber	30227.000000	1.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000
InstanceNumber	30227.000000	1.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000
PatientOrientation	0.000000	nan	nan	nan	nan	nan	nan	nan
SamplesPerPixel	30227.000000	1.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000
Rows	30227.000000	1024.000000	0.000000	1024.000000	1024.000000	1024.000000	1024.000000	1024.000000
Columns	30227.000000	1024.000000	0.000000	1024.000000	1024.000000	1024.000000	1024.000000	1024.000000
MultiPixelSpacing	30227.000000	1.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000
PixelSpacing	30227.000000	0.155481	0.015823	0.115000	0.143000	0.143000	0.168000	0.198800
PixelSpacing1	30227.000000	0.155481	0.015823	0.115000	0.143000	0.143000	0.168000	0.198800
BitsAllocated	30227.000000	8.000000	0.000000	8.000000	8.000000	8.000000	8.000000	8.000000
BitsStored	30227.000000	8.000000	0.000000	8.000000	8.000000	8.000000	8.000000	8.000000
HighBit	30227.000000	7.000000	0.000000	7.000000	7.000000	7.000000	7.000000	7.000000
PixelRepresentation	30227.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
LossyImageCompression	30227.000000	1.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000
img_min	30227.000000	0.365931	2.097720	0.000000	0.000000	0.000000	0.000000	95.000000
img_max	30227.000000	-8.060740	13.975120	-110.000000	-10.000000	-1.000000	-1.000000	-1.000000
img_mean	30227.000000	124.547933	21.551214	26.417231	110.284383	119.870465	136.768286	229.147834
img_std	30227.000000	58.048557	10.772212	18.836551	50.516496	58.984575	66.681578	99.117195
img_pct_window	30227.000000	0.215550	0.098674	0.000000	0.144041	0.216644	0.278998	0.919351

Image Name	No. Of Bounding Boxes	X	Y	Width	Height
17d34b01-a492-4764-9ee7-1403ed7a3f97	1	636.000000	498.000000	278.000000	288.000000
ba29c8c8-c116-4b8e-b8d6-ec870ca9f9e5	1	272.000000	457.000000	219.000000	364.000000
1b7f9ebf-bd1c-4195-9186-d6adb65945ee	1	348.000000	313.000000	122.000000	383.000000
ee8a4128-a907-475b-a363-9cd60d65e46e	2	644.000000	434.000000	182.000000	347.000000
ee8a4128-a907-475b-a363-9cd60d65e46e	2	212.000000	365.000000	184.000000	378.000000
9ded4d8b-5e88-4470-bb20-1d55d76d6658	2	467.000000	513.000000	315.000000	363.000000
9ded4d8b-5e88-4470-bb20-1d55d76d6658	2	18.000000	264.000000	306.000000	542.000000
617d8a06-9de7-4b9e-b7c8-a9c234a44602	1	634.000000	483.000000	201.000000	147.000000
9d82f9f6-b84d-48d0-9ce5-68329990d7fa	1	229.000000	348.000000	221.000000	221.000000
750d6b01-4273-43b2-8de8-500e738cb257	1	383.000000	351.000000	115.000000	186.000000
74340139-eebf-445a-a0a1-2b9101233a3a	1	662.000000	468.000000	156.000000	165.000000
0c89ca38-27ca-4cd0-88cb-18093fdcb04b	1	153.000000	369.000000	257.000000	181.000000
822ac3a2-71b5-4540-9139-2dcca518f09f	2	651.000000	420.000000	265.000000	426.000000
822ac3a2-71b5-4540-9139-2dcca518f09f	2	281.000000	148.000000	270.000000	770.000000
b3f132cb-438b-40d4-a951-0089e8b1a840	2	166.000000	230.000000	271.000000	606.000000
b3f132cb-438b-40d4-a951-0089e8b1a840	2	556.000000	345.000000	256.000000	563.000000
66ab31a6-be10-4f4e-ad4d-b4aeb9371271	1	367.000000	423.000000	131.000000	181.000000
844ac956-e1e2-4f27-b58c-390d6405c3a0	2	429.000000	59.000000	359.000000	692.000000
844ac956-e1e2-4f27-b58c-390d6405c3a0	2	16.000000	60.000000	386.000000	631.000000
d50e7c06-768f-4943-8e00-392d664fe580	1	187.000000	259.000000	278.000000	432.000000
23ca0450-4138-4e7a-9489-0f5b6a91031d	2	621.000000	399.000000	193.000000	91.000000
23ca0450-4138-4e7a-9489-0f5b6a91031d	2	94.000000	414.000000	224.000000	134.000000
37f5c61c-c395-468f-a4ea-7108343a22cb	2	166.000000	158.000000	300.000000	652.000000
37f5c61c-c395-468f-a4ea-7108343a22cb	2	588.000000	178.000000	265.000000	630.000000
d72cd242-ad82-480f-acd1-e1b3a793cad2	2	662.000000	275.000000	208.000000	339.000000
d72cd242-ad82-480f-acd1-e1b3a793cad2	2	195.000000	409.000000	213.000000	363.000000
1ef28712-7b54-49de-838a-3a56326d26a0	1	568.000000	481.000000	194.000000	173.000000
b62a6a88-6ad1-43cf-ad59-6d171e89b265	1	587.000000	340.000000	250.000000	335.000000

	Folder	Image_Class	Image_file	Full_filename	Image_Class_Category	Image_Target
17686	Training	Not Normal	59cdf26b-d2de-436b-9d67-4c8317ea46be.dcm	/content/drive/MyDrive/Colab Notebooks/Project/Capstone Project//Training/Not Normal/59cdf26b-d2de-436b-9d67-4c8317ea46be.dcm	2	0
18263	Training	Not Normal	6477a41c-821b-4c21-88cf-677f73d8bcd4.dcm	/content/drive/MyDrive/Colab Notebooks/Project/Capstone Project//Training/Not Normal/6477a41c-821b-4c21-88cf-677f73d8bcd4.dcm	2	0
19141	Training	Not Normal	71ecae41-9818-4f71-a2c6-9e237efca012.dcm	/content/drive/MyDrive/Colab Notebooks/Project/Capstone Project//Training/Not Normal/71ecae41-9818-4f71-a2c6-9e237efca012.dcm	2	0
3091	Training	Lung Opacity	9c0fbb4b-715d-43ad-ac16-3cd798caaab3.dcm	/content/drive/MyDrive/Colab Notebooks/Project/Capstone Project//Training/Lung Opacity/9c0fbb4b-715d-43ad-ac16-3cd798caaab3.dcm	0	1
4958	Training	Lung Opacity	d60b0cba-c1bd-4741-86ef-c24760605b03.dcm	/content/drive/MyDrive/Colab Notebooks/Project/Capstone Project//Training/Lung Opacity/d60b0cba-c1bd-4741-86ef-c24760605b03.dcm	0	1
14551	Training	Normal	0103fadb-1663-40a6-8a9e-09d626cd2091.dcm	/content/drive/MyDrive/Colab Notebooks/Project/Capstone Project//Training/Normal/0103fadb-1663-40a6-8a9e-09d626cd2091.dcm	1	0
2617	Training	Lung Opacity	85f49d66-c50b-4f89-be34-2aee655583b3.dcm	/content/drive/MyDrive/Colab Notebooks/Project/Capstone Project//Training/Lung Opacity/85f49d66-c50b-4f89-be34-2aee655583b3.dcm	0	1
16289	Training	Not Normal	43d0854c-a7f1-4901-addc-fa88cecf99d5.dcm	/content/drive/MyDrive/Colab Notebooks/Project/Capstone Project//Training/Not Normal/43d0854c-a7f1-4901-addc-fa88cecf99d5.dcm	2	0
25559	Training	Not Normal	f36259c8-6751-4c6f-b9d4-44fd4f86e61b.dcm	/content/drive/MyDrive/Colab Notebooks/Project/Capstone Project//Training/Not Normal/f36259c8-6751-4c6f-b9d4-44fd4f86e61b.dcm	2	0
4198	Training	Lung Opacity	b8d7b42e-8dc7-4330-a8f3-db52852f1714.dcm	/content/drive/MyDrive/Colab Notebooks/Project/Capstone Project//Training/Lung Opacity/b8d7b42e-8dc7-4330-a8f3-db52852f1714.dcm	0	1

MODEL_NAME	LOSS	ACCURACY
model	67.160000	71.250000
model_2	70.900000	68.170000
model_2a	74.990000	67.750000
model_3	59.180000	69.580000

MODEL_NAME	LOSS	ACCURACY
model	53.700000	74.100000
model_2	76.910000	65.650000
model_2a	55.800000	74.500000
model_3	52.840000	72.950000

NAMES_JPG	NAMES_DCM	Image_Class	Image_Class_Category	Image_Target	Full_filename
bc03198b-57f4-47b5-8808-114fd5cb9808.jpg	bc03198b-57f4-47b5-8808-114fd5cb9808.dcm	Lung Opacity	0	1	Training_jpg/bc03198b-57f4-47b5-8808-114fd5cb9808.jpg
db5c4b38-0c83-4f5b-a159-ea4c3e1bc7a8.jpg	db5c4b38-0c83-4f5b-a159-ea4c3e1bc7a8.dcm	Not Normal	2	0	Training_jpg/db5c4b38-0c83-4f5b-a159-ea4c3e1bc7a8.jpg
e94d8646-1703-44db-9062-c9b305e238f0.jpg	e94d8646-1703-44db-9062-c9b305e238f0.dcm	Lung Opacity	0	1	Training_jpg/e94d8646-1703-44db-9062-c9b305e238f0.jpg
e63932eb-033b-46f1-aa55-6e913ccbe03a.jpg	e63932eb-033b-46f1-aa55-6e913ccbe03a.dcm	Not Normal	2	0	Training_jpg/e63932eb-033b-46f1-aa55-6e913ccbe03a.jpg
7adb12c0-b1b2-402e-9c58-5a8873eef61d.jpg	7adb12c0-b1b2-402e-9c58-5a8873eef61d.dcm	Lung Opacity	0	1	Training_jpg/7adb12c0-b1b2-402e-9c58-5a8873eef61d.jpg
3e881f10-28aa-4626-a79a-50cc014b7a1a.jpg	3e881f10-28aa-4626-a79a-50cc014b7a1a.dcm	Lung Opacity	0	1	Training_jpg/3e881f10-28aa-4626-a79a-50cc014b7a1a.jpg
8f719c99-d7f6-4e27-a5f6-3a259dc60b61.jpg	8f719c99-d7f6-4e27-a5f6-3a259dc60b61.dcm	Normal	1	0	Training_jpg/8f719c99-d7f6-4e27-a5f6-3a259dc60b61.jpg
a99561a5-2c12-48cd-be57-ce6b6cf58da1.jpg	a99561a5-2c12-48cd-be57-ce6b6cf58da1.dcm	Lung Opacity	0	1	Training_jpg/a99561a5-2c12-48cd-be57-ce6b6cf58da1.jpg
01fe92f7-ff87-4f9e-9077-e00e670d1b47.jpg	01fe92f7-ff87-4f9e-9077-e00e670d1b47.dcm	Normal	1	0	Training_jpg/01fe92f7-ff87-4f9e-9077-e00e670d1b47.jpg
091d75a8-e23e-4cb8-af67-10cb314077ec.jpg	091d75a8-e23e-4cb8-af67-10cb314077ec.dcm	Lung Opacity	0	1	Training_jpg/091d75a8-e23e-4cb8-af67-10cb314077ec.jpg
d60394b5-1f55-4929-a054-abd1eaa5d088.jpg	d60394b5-1f55-4929-a054-abd1eaa5d088.dcm	Normal	1	0	Training_jpg/d60394b5-1f55-4929-a054-abd1eaa5d088.jpg
a512e598-45c5-4f16-b0b0-63f05f6afb14.jpg	a512e598-45c5-4f16-b0b0-63f05f6afb14.dcm	Not Normal	2	0	Training_jpg/a512e598-45c5-4f16-b0b0-63f05f6afb14.jpg
c1c3ec5d-20ba-42f7-91f9-48032d97ffc9.jpg	c1c3ec5d-20ba-42f7-91f9-48032d97ffc9.dcm	Normal	1	0	Training_jpg/c1c3ec5d-20ba-42f7-91f9-48032d97ffc9.jpg
34f16115-b927-44eb-9880-b413b65421fa.jpg	34f16115-b927-44eb-9880-b413b65421fa.dcm	Lung Opacity	0	1	Training_jpg/34f16115-b927-44eb-9880-b413b65421fa.jpg
20fa542a-4930-46eb-8e1b-3e938980e6ea.jpg	20fa542a-4930-46eb-8e1b-3e938980e6ea.dcm	Lung Opacity	0	1	Training_jpg/20fa542a-4930-46eb-8e1b-3e938980e6ea.jpg
e660384d-3cd7-4a42-bfa3-c64552f55e33.jpg	e660384d-3cd7-4a42-bfa3-c64552f55e33.dcm	Not Normal	2	0	Training_jpg/e660384d-3cd7-4a42-bfa3-c64552f55e33.jpg
3abb7176-035d-46cc-844e-820870e8154b.jpg	3abb7176-035d-46cc-844e-820870e8154b.dcm	Lung Opacity	0	1	Training_jpg/3abb7176-035d-46cc-844e-820870e8154b.jpg
05b2d676-777a-4a4d-82ab-bf3f5f9b4d60.jpg	05b2d676-777a-4a4d-82ab-bf3f5f9b4d60.dcm	Lung Opacity	0	1	Training_jpg/05b2d676-777a-4a4d-82ab-bf3f5f9b4d60.jpg
8176c9ad-ffde-4b9c-9050-b5b725ed4c0d.jpg	8176c9ad-ffde-4b9c-9050-b5b725ed4c0d.dcm	Normal	1	0	Training_jpg/8176c9ad-ffde-4b9c-9050-b5b725ed4c0d.jpg
8c98c399-dc7d-4466-a9b4-fe017b51016e.jpg	8c98c399-dc7d-4466-a9b4-fe017b51016e.dcm	Normal	1	0	Training_jpg/8c98c399-dc7d-4466-a9b4-fe017b51016e.jpg

	Folder	Image_Class	Image_file	Full_filename	Image_Class_Category	Image_Target
9063	Training	Normal	6927a615-8ec4-4219-aa92-e368d628cb2e.dcm	Training/Normal/6927a615-8ec4-4219-aa92-e368d628cb2e.dcm	1	0
13219	Training	Normal	d5011cb2-a146-4ca6-a055-2ec956315350.dcm	Training/Normal/d5011cb2-a146-4ca6-a055-2ec956315350.dcm	1	0
17121	Training	Not Normal	4c2749cb-7bb8-46d8-9d14-491d0035f891.dcm	Training/Not Normal/4c2749cb-7bb8-46d8-9d14-491d0035f891.dcm	2	0
17925	Training	Not Normal	58b049d6-b48c-4edc-ac83-753591aa359d.dcm	Training/Not Normal/58b049d6-b48c-4edc-ac83-753591aa359d.dcm	2	0
4927	Training	Lung Opacity	c04f9256-7358-42ef-b843-19dbf64ef287.dcm	Training/Lung Opacity/c04f9256-7358-42ef-b843-19dbf64ef287.dcm	0	1
24237	Training	Not Normal	d89f8d29-3832-4d89-8647-d16f9079c25a.dcm	Training/Not Normal/d89f8d29-3832-4d89-8647-d16f9079c25a.dcm	2	0
667	Training	Lung Opacity	1c44e0a4-4612-438f-9a83-8d5bf919cb67.dcm	Training/Lung Opacity/1c44e0a4-4612-438f-9a83-8d5bf919cb67.dcm	0	1
23130	Training	Not Normal	c76bb89f-781d-4c53-8685-58f981394a09.dcm	Training/Not Normal/c76bb89f-781d-4c53-8685-58f981394a09.dcm	2	0
8488	Training	Normal	5bfa4ba1-9ea6-42c5-b44f-1a58dfe648bf.dcm	Training/Normal/5bfa4ba1-9ea6-42c5-b44f-1a58dfe648bf.dcm	1	0
23058	Training	Not Normal	c648be1a-3426-414b-80af-fd4cbdffa7f2.dcm	Training/Not Normal/c648be1a-3426-414b-80af-fd4cbdffa7f2.dcm	2	0
8358	Training	Normal	58bf27e0-a307-4d29-a977-511340f4572f.dcm	Training/Normal/58bf27e0-a307-4d29-a977-511340f4572f.dcm	1	0
19026	Training	Not Normal	6bb95d1b-a502-430f-ba27-550549d70de3.dcm	Training/Not Normal/6bb95d1b-a502-430f-ba27-550549d70de3.dcm	2	0
25695	Training	Not Normal	f09d8288-c4f7-480d-a24d-b1c54c5ede45.dcm	Training/Not Normal/f09d8288-c4f7-480d-a24d-b1c54c5ede45.dcm	2	0
17748	Training	Not Normal	55467f65-236a-4b29-b7e3-6de1e1c8f973.dcm	Training/Not Normal/55467f65-236a-4b29-b7e3-6de1e1c8f973.dcm	2	0
9952	Training	Normal	7fbf1fe8-f97d-4e7c-9ac2-65d4e06f33b4.dcm	Training/Normal/7fbf1fe8-f97d-4e7c-9ac2-65d4e06f33b4.dcm	1	0
22298	Training	Not Normal	ad1ab5c2-fe92-498e-b82f-9464f2140c43.dcm	Training/Not Normal/ad1ab5c2-fe92-498e-b82f-9464f2140c43.dcm	2	0
2377	Training	Lung Opacity	6cba26dc-51c2-4a3a-bbd7-3af8dd5bfd65.dcm	Training/Lung Opacity/6cba26dc-51c2-4a3a-bbd7-3af8dd5bfd65.dcm	0	1
8432	Training	Normal	5a9235a7-8034-4115-9c0a-9612ef02f968.dcm	Training/Normal/5a9235a7-8034-4115-9c0a-9612ef02f968.dcm	1	0
7314	Training	Normal	3b81bcea-ee88-4c13-b4f6-2a53161d2b22.dcm	Training/Normal/3b81bcea-ee88-4c13-b4f6-2a53161d2b22.dcm	1	0
18306	Training	Not Normal	5fddd203-a282-4f29-b741-dd03bfcec835.dcm	Training/Not Normal/5fddd203-a282-4f29-b741-dd03bfcec835.dcm	2	0

	NAMES_JPG	NAMES_DCM	Image_Class	Image_Class_Category	Image_Target	Full_filename
5075	b8328cd5-8b9d-4834-aa79-053be9640655.jpg	b8328cd5-8b9d-4834-aa79-053be9640655.dcm	Lung Opacity	0	1	Training_jpg/b8328cd5-8b9d-4834-aa79-053be9640655.jpg
1960	468feb51-e690-4ba0-967d-62dec940b671.jpg	468feb51-e690-4ba0-967d-62dec940b671.dcm	Not Normal	2	0	Training_jpg/468feb51-e690-4ba0-967d-62dec940b671.jpg
2978	9a7bcfc0-6ccf-4d8f-9b02-ad56448761c5.jpg	9a7bcfc0-6ccf-4d8f-9b02-ad56448761c5.dcm	Not Normal	2	0	Training_jpg/9a7bcfc0-6ccf-4d8f-9b02-ad56448761c5.jpg
1726	c4542463-c7e3-4af7-a6d2-bb6cf6388012.jpg	c4542463-c7e3-4af7-a6d2-bb6cf6388012.dcm	Not Normal	2	0	Training_jpg/c4542463-c7e3-4af7-a6d2-bb6cf6388012.jpg
5407	57bffc6f-1656-45a2-9e49-e28d1dcbeee1.jpg	57bffc6f-1656-45a2-9e49-e28d1dcbeee1.dcm	Lung Opacity	0	1	Training_jpg/57bffc6f-1656-45a2-9e49-e28d1dcbeee1.jpg
5272	d5e9a92d-fb2f-416b-8731-4f7435d6c9c5.jpg	d5e9a92d-fb2f-416b-8731-4f7435d6c9c5.dcm	Lung Opacity	0	1	Training_jpg/d5e9a92d-fb2f-416b-8731-4f7435d6c9c5.jpg
724	9aeac4e0-68d4-4214-9e22-72facf654789.jpg	9aeac4e0-68d4-4214-9e22-72facf654789.dcm	Not Normal	2	0	Training_jpg/9aeac4e0-68d4-4214-9e22-72facf654789.jpg
5247	df21b6dd-32b7-4edd-8b7b-fa1a35e0c043.jpg	df21b6dd-32b7-4edd-8b7b-fa1a35e0c043.dcm	Lung Opacity	0	1	Training_jpg/df21b6dd-32b7-4edd-8b7b-fa1a35e0c043.jpg
4998	b7136f3f-41da-4b16-b7c3-4b929d81e6a5.jpg	b7136f3f-41da-4b16-b7c3-4b929d81e6a5.dcm	Lung Opacity	0	1	Training_jpg/b7136f3f-41da-4b16-b7c3-4b929d81e6a5.jpg
4789	348699f9-4eb2-42b7-b9e8-8bc3230b32a5.jpg	348699f9-4eb2-42b7-b9e8-8bc3230b32a5.dcm	Lung Opacity	0	1	Training_jpg/348699f9-4eb2-42b7-b9e8-8bc3230b32a5.jpg
1404	89ff3f4d-2631-42d0-8d82-8017c4dcce7c.jpg	89ff3f4d-2631-42d0-8d82-8017c4dcce7c.dcm	Not Normal	2	0	Training_jpg/89ff3f4d-2631-42d0-8d82-8017c4dcce7c.jpg
5063	ac18858f-cb0a-4d54-89a0-25a8c2bc0980.jpg	ac18858f-cb0a-4d54-89a0-25a8c2bc0980.dcm	Lung Opacity	0	1	Training_jpg/ac18858f-cb0a-4d54-89a0-25a8c2bc0980.jpg
486	95ef1bd3-c207-4d70-88fd-c37c275086bb.jpg	95ef1bd3-c207-4d70-88fd-c37c275086bb.dcm	Normal	1	0	Training_jpg/95ef1bd3-c207-4d70-88fd-c37c275086bb.jpg
3464	0b0073e0-9c19-4da4-88f2-b3d2ebc6f1f4.jpg	0b0073e0-9c19-4da4-88f2-b3d2ebc6f1f4.dcm	Lung Opacity	0	1	Training_jpg/0b0073e0-9c19-4da4-88f2-b3d2ebc6f1f4.jpg
3386	722ad9c3-919a-4c08-bb67-cafd603ba754.jpg	722ad9c3-919a-4c08-bb67-cafd603ba754.dcm	Lung Opacity	0	1	Training_jpg/722ad9c3-919a-4c08-bb67-cafd603ba754.jpg
5640	9cde0ff6-095f-4ac6-88a8-c9ffc4676230.jpg	9cde0ff6-095f-4ac6-88a8-c9ffc4676230.dcm	Lung Opacity	0	1	Training_jpg/9cde0ff6-095f-4ac6-88a8-c9ffc4676230.jpg
329	d0654ae8-b032-44b4-93e1-399dfd5dde26.jpg	d0654ae8-b032-44b4-93e1-399dfd5dde26.dcm	Not Normal	2	0	Training_jpg/d0654ae8-b032-44b4-93e1-399dfd5dde26.jpg
5822	aec75bea-7a4a-4ca6-8e82-bf3f05963c9d.jpg	aec75bea-7a4a-4ca6-8e82-bf3f05963c9d.dcm	Lung Opacity	0	1	Training_jpg/aec75bea-7a4a-4ca6-8e82-bf3f05963c9d.jpg
5778	b0604ee6-5f21-4b14-98ca-3bedaccd4e10.jpg	b0604ee6-5f21-4b14-98ca-3bedaccd4e10.dcm	Lung Opacity	0	1	Training_jpg/b0604ee6-5f21-4b14-98ca-3bedaccd4e10.jpg
3672	f9538c72-0f7b-45e3-98a8-de3e4434fe4d.jpg	f9538c72-0f7b-45e3-98a8-de3e4434fe4d.dcm	Lung Opacity	0	1	Training_jpg/f9538c72-0f7b-45e3-98a8-de3e4434fe4d.jpg

	NAMES_JPG	NAMES_DCM	Image_Class	Image_Class_Category	Image_Target	Full_filename
3557	6a41e296-8bda-4f84-8766-c253246b77d0.jpg	6a41e296-8bda-4f84-8766-c253246b77d0.dcm	Lung Opacity	0	1	Training_jpg/6a41e296-8bda-4f84-8766-c253246b77d0.jpg
3350	bc234c2b-96aa-4263-931b-b7420c235daa.jpg	bc234c2b-96aa-4263-931b-b7420c235daa.dcm	Lung Opacity	0	1	Training_jpg/bc234c2b-96aa-4263-931b-b7420c235daa.jpg
5344	36cf3250-2ea6-4ef9-8143-c1bbafe83475.jpg	36cf3250-2ea6-4ef9-8143-c1bbafe83475.dcm	Lung Opacity	0	1	Training_jpg/36cf3250-2ea6-4ef9-8143-c1bbafe83475.jpg
3575	c3670fac-87af-4e1b-a1d4-3181427a60fc.jpg	c3670fac-87af-4e1b-a1d4-3181427a60fc.dcm	Lung Opacity	0	1	Training_jpg/c3670fac-87af-4e1b-a1d4-3181427a60fc.jpg
2021	fe4d9dea-1e2e-4ef4-886d-bf9a668b7850.jpg	fe4d9dea-1e2e-4ef4-886d-bf9a668b7850.dcm	Not Normal	2	0	Training_jpg/fe4d9dea-1e2e-4ef4-886d-bf9a668b7850.jpg
64	d5dec418-0f74-489f-b696-ce73e6016e40.jpg	d5dec418-0f74-489f-b696-ce73e6016e40.dcm	Normal	1	0	Training_jpg/d5dec418-0f74-489f-b696-ce73e6016e40.jpg
4488	ca6ed544-2804-4f23-97a6-5a9f4814e7c1.jpg	ca6ed544-2804-4f23-97a6-5a9f4814e7c1.dcm	Lung Opacity	0	1	Training_jpg/ca6ed544-2804-4f23-97a6-5a9f4814e7c1.jpg
2316	6e9329fc-862a-47dc-848f-fb560ed17096.jpg	6e9329fc-862a-47dc-848f-fb560ed17096.dcm	Not Normal	2	0	Training_jpg/6e9329fc-862a-47dc-848f-fb560ed17096.jpg
3255	add89c20-44cf-461a-9857-c41d753d4062.jpg	add89c20-44cf-461a-9857-c41d753d4062.dcm	Lung Opacity	0	1	Training_jpg/add89c20-44cf-461a-9857-c41d753d4062.jpg
1351	e5ffd138-39af-4e75-a79f-3531f2ff1b9e.jpg	e5ffd138-39af-4e75-a79f-3531f2ff1b9e.dcm	Normal	1	0	Training_jpg/e5ffd138-39af-4e75-a79f-3531f2ff1b9e.jpg

MODEL_NAME	LOSS	ACCURACY
model	75.890000	69.000000
model_2	59.700000	70.500000
model_2a	61.510000	68.670000
model_3	53.460000	73.830000

MODEL_NAME	LOSS	ACCURACY
model	53.580000	73.250000
model_2	61.660000	71.830000
model_2a	58.060000	70.580000
model_3	56.600000	72.250000

MODEL_NAME	LOSS	ACCURACY
model	55.070000	73.830000
model_2	61.080000	67.920000
model_2a	54.650000	73.080000
model_3	56.000000	73.250000

MODEL_NAME	LOSS	ACCURACY
model	59.080000	71.420000
model_2	56.940000	70.500000
model_2a	55.570000	71.830000
model_3	57.460000	69.830000

MODEL_NAME	LOSS	ACCURACY
model	63.820000	69.170000
model_2	69.310000	50.000000
model_2a	64.930000	50.000000
model_3	69.330000	50.000000

MODEL_NAME	LOSS	ACCURACY
model	61.090000	71.500000
model_2	69.320000	50.000000
model_2a	56.260000	71.580000
model_3	69.320000	50.000000

MODEL_NAME	LOSS	ACCURACY
model	24851053.120000	68.670000
model_2	10704.870000	50.670000
model_2a	8354901.560000	69.920000
model_3	57.600000	70.670000

Capstone Project - Computer Vision Pneumonia Detection Challenge¶

Printing Machine Information:¶

Project Objective: Design a DL based algorithm for detecting pneumonia¶

1. Milestone 1:¶

Step 1: Import the data.¶

Step 2: Map training and testing images to its classes.¶

Step 3: Map training and testing images to its annotations.¶

Step 4: Preprocessing and Visualisation of different classes¶

Step 5: Display images with bounding box.¶

Step 6: Design, train and test basic CNN models for classification.¶

2. Milestone 2:¶

Input: Preprocessed output from Milestone-1¶

Process:¶

Step 1: Fine tune the trained basic CNN models for classification.¶

Fine Tuning 1:¶

Increase the number of images to 10000 and train the models¶

Fine Tuning 2:¶

Convert the images to JPEG and train the models on converted images¶

Fine Tuning 3.1:¶

Resize the JPEG images to 128x128 and train the models:¶

Fine Tuning 3.2:¶

Resize the DICOM images to 128x128 and train the models:¶

Fine Tuning 4:¶

Modify optimizer parameters and changing loss function¶

Fine Tuning 5:¶

Reduce the number of images per batch¶

Fine Tuning 6:¶

Increase images per batch:¶

Fine Tuning 7:¶

Add / Update model layers:¶

Fine Tuning 8:¶

Using RMSProp Optimizer:¶

Fine Tuning 9:¶

Using Image Data Generator¶

Building Final Model:¶

Step 2: Apply Transfer Learning model for classification¶

Transfer learning using VGG 16¶

Transfer Learning using RestNet50¶

Transfer Learning using InceptionV3:¶

Transfer Learning using DenseNet121:¶

Transfer Learning - Using DenseNet121 with ChexNet Weights¶

Step 3: Design, train and test RCNN & its hybrids based object detection models to impose the bounding box or mask over the area of interest.¶

Object Detection:¶

Designing a MobileNet Architecture¶

Faster RCNN¶

Step 4: Pickle the model for future prediction¶

Importing the test images¶

Step 5: Final Report¶

Observations:¶

*** THE END **¶

***** THE END ¶

MODEL_NAME	LOSS	ACCURACY
model	28070.410000	50.000000
model_2	135.950000	50.750000
model_2a	770954.590000	50.000000
model_3	62.970000	70.830000

	NAMES_JPG	NAMES_DCM	Image_Class	Image_Class_Category	Image_Target	Full_filename	Target_Cat
3272	da0d705d-7722-43d1-a880-a34c553f0719.jpg	da0d705d-7722-43d1-a880-a34c553f0719.dcm	Lung Opacity	0	1	Training_jpg/da0d705d-7722-43d1-a880-a34c553f0719.jpg	Yes
5152	00704310-78a8-4b38-8475-49f4573b2dbb.jpg	00704310-78a8-4b38-8475-49f4573b2dbb.dcm	Lung Opacity	0	1	Training_jpg/00704310-78a8-4b38-8475-49f4573b2dbb.jpg	Yes

	NAMES_JPG	NAMES_DCM	Image_Class	Image_Class_Category	Image_Target	Full_filename	Target_Cat
23	6326e2e5-d349-42e2-b42e-8dd63bc40d19.jpg	6326e2e5-d349-42e2-b42e-8dd63bc40d19.dcm	Not Normal	2	0	Training_jpg/6326e2e5-d349-42e2-b42e-8dd63bc40d19.jpg	No
4737	be9d0ed7-00ce-44b5-a83c-8a9796be8ea6.jpg	be9d0ed7-00ce-44b5-a83c-8a9796be8ea6.dcm	Lung Opacity	0	1	Training_jpg/be9d0ed7-00ce-44b5-a83c-8a9796be8ea6.jpg	Yes
1327	657b1ec2-8f10-4070-bfde-db020bbdb2fe.jpg	657b1ec2-8f10-4070-bfde-db020bbdb2fe.dcm	Normal	1	0	Training_jpg/657b1ec2-8f10-4070-bfde-db020bbdb2fe.jpg	No

MODEL_NAME	LOSS	ACCURACY
model	29586746.880000	63.330000
model_2	1453439.450000	50.000000
model_2a	14527342.190000	61.420000
model_3	60.620000	68.670000

Model_Name	Accuracy	Precision	Recall	F1 Score	ROC
VGG 16	0.712500	0.704655	0.731667	0.717907	0.712500
VGG 16 Using IDG	0.663333	0.741379	0.501667	0.598410	0.663333
RESTNET50	0.718333	0.707937	0.743333	0.725203	0.718333
RESTNET50_IMAGE DATA GENERATOR	0.715000	0.723958	0.695000	0.709184	0.715000
INCEPTION V3	0.636667	0.652985	0.583333	0.616197	0.636667
INCEPTION V3_IMAGE DATA GENERATOR	0.585833	0.554040	0.880000	0.679974	0.585833
INCEPTION V3 - ADDED LAYERS	0.688333	0.687708	0.690000	0.688852	0.688333
DENSENET121	0.730833	0.727422	0.738333	0.732837	0.730833

	patientId	x	y	width	height	Target	Full_filename	Filename
223	081a1f56-6c47-4b80-988a-3d357c977d65	559.0	393.0	289.0	502.0	1	stage_2_train_images/081a1f56-6c47-4b80-988a-3d357c977d65.dcm	081a1f56-6c47-4b80-988a-3d357c977d65.dcm
544	11750ff6-94ea-43d0-bad8-f3c3e5278d7d	570.0	395.0	271.0	499.0	1	stage_2_train_images/11750ff6-94ea-43d0-bad8-f3c3e5278d7d.dcm	11750ff6-94ea-43d0-bad8-f3c3e5278d7d.dcm

	patientId	x	y	width	height	Target	Full_filename	Filename
2785	56b5c0cf-8a3c-42f2-b3e2-adb2f550c451	81.000000	587.000000	210.000000	222.000000	1	stage_2_train_images/56b5c0cf-8a3c-42f2-b3e2-adb2f550c451.dcm	56b5c0cf-8a3c-42f2-b3e2-adb2f550c451.dcm
2546	4b85f617-4e30-404c-b9c1-5e9b8776dfba	678.000000	290.000000	249.000000	506.000000	1	stage_2_train_images/4b85f617-4e30-404c-b9c1-5e9b8776dfba.dcm	4b85f617-4e30-404c-b9c1-5e9b8776dfba.dcm
3112	64917123-823f-4c01-a0d6-06bdfd899a6c	242.000000	260.000000	240.000000	507.000000	1	stage_2_train_images/64917123-823f-4c01-a0d6-06bdfd899a6c.dcm	64917123-823f-4c01-a0d6-06bdfd899a6c.dcm
908	2635357b-8865-4551-9ed8-3f90b996c982	225.000000	489.000000	164.000000	115.000000	1	stage_2_train_images/2635357b-8865-4551-9ed8-3f90b996c982.dcm	2635357b-8865-4551-9ed8-3f90b996c982.dcm
2412	463c04a5-94b8-4969-a725-a58c2b6211c1	599.000000	309.000000	282.000000	546.000000	1	stage_2_train_images/463c04a5-94b8-4969-a725-a58c2b6211c1.dcm	463c04a5-94b8-4969-a725-a58c2b6211c1.dcm
4466	8d48694a-f998-4043-bc98-63a3c18527f2	481.000000	232.000000	329.000000	642.000000	1	stage_2_train_images/8d48694a-f998-4043-bc98-63a3c18527f2.dcm	8d48694a-f998-4043-bc98-63a3c18527f2.dcm
1883	3b94276b-4b65-4339-a629-0500f0171c45	621.000000	572.000000	242.000000	194.000000	1	stage_2_train_images/3b94276b-4b65-4339-a629-0500f0171c45.dcm	3b94276b-4b65-4339-a629-0500f0171c45.dcm
4114	84d73e68-ceb9-4bff-94d9-9b97523109ee	115.000000	509.000000	370.000000	286.000000	1	stage_2_train_images/84d73e68-ceb9-4bff-94d9-9b97523109ee.dcm	84d73e68-ceb9-4bff-94d9-9b97523109ee.dcm
4378	8b252ccb-1a24-47cf-bd2d-b42c6c9a2557	235.000000	236.000000	227.000000	627.000000	1	stage_2_train_images/8b252ccb-1a24-47cf-bd2d-b42c6c9a2557.dcm	8b252ccb-1a24-47cf-bd2d-b42c6c9a2557.dcm
2934	5c0bb75f-a4d1-4070-b40d-6fff71138147	225.000000	634.000000	128.000000	96.000000	1	stage_2_train_images/5c0bb75f-a4d1-4070-b40d-6fff71138147.dcm	5c0bb75f-a4d1-4070-b40d-6fff71138147.dcm

	patientId	x	y	width	height
1904	3bd6f6e0-c07a-4c22-87e7-53d60de9ad0e	142.000000	265.000000	228.000000	503.000000
3244	6a82ef54-9e27-4c55-afba-47298ad088c9	236.000000	512.000000	113.000000	90.000000
2912	5b72c6d2-e7eb-44a6-b625-78d2ec0008f2	170.000000	414.000000	177.000000	455.000000
932	29b9a5ae-56e4-4e15-a78d-cbaeda0c276a	722.000000	567.000000	139.000000	238.000000
1351	34e283d8-3c14-4d47-8882-fb53ec242f87	227.000000	207.000000	250.000000	570.000000
3022	5fe137e2-d416-4f49-b685-6a3fe5367910	160.000000	408.000000	184.000000	412.000000
2645	4fa69712-d57b-4ea9-bdf3-40cdc03f02ff	309.000000	230.000000	233.000000	441.000000
4038	826a4142-b079-4825-85f7-5fd69292e4cf	453.000000	231.000000	284.000000	590.000000
2420	4668a90c-d352-47d0-a3ac-a54aa8ce053b	162.000000	611.000000	230.000000	235.000000
522	0f831256-0a69-4fe5-9719-1563d2b6b7b9	166.000000	535.000000	134.000000	279.000000

	patientId	PredictionString
1222	1bb7ca9c-c1ea-4054-93ee-44009e6cc119	0.5 0 0 100 100
289	04176263-de7f-471b-9dc1-7a5af4e6d943	0.5 0 0 100 100
1860	243847a8-928b-4fb9-a7e5-20ec95e0445e	0.5 0 0 100 100
1388	1e3497cb-00c5-4333-9d35-a27fc9a49829	0.5 0 0 100 100
2865	c00a132f-6108-46e7-a67a-77efd3d2d0ef	0.5 0 0 100 100

	PatientID	Image_file
375	0550c5d5-941c-4995-9276-6401a8a4ffbe	0550c5d5-941c-4995-9276-6401a8a4ffbe.dcm
1535	1fe97b7f-62e1-4b0c-84b4-d41486039f01	1fe97b7f-62e1-4b0c-84b4-d41486039f01.dcm
1962	2598af1b-4bfa-470c-95d5-3ba472f033b6	2598af1b-4bfa-470c-95d5-3ba472f033b6.dcm
1170	1b06bd36-85c7-4495-b0a8-a9aacc29be17	1b06bd36-85c7-4495-b0a8-a9aacc29be17.dcm
2740	2ff6f390-1e4c-489e-91ac-6b18448a08db	2ff6f390-1e4c-489e-91ac-6b18448a08db.dcm